Device, System, and Method of Autonomous Driving and Tele-Operated Vehicles

ABSTRACT

Device, system, and method of autonomous driving and tele-operated vehicles. A vehicular Artificial Intelligence (AI) unit, is configured: to receive inputs from a plurality of vehicular sensors of a vehicle; to locally process within the vehicle at least a first portion of the inputs; to wirelessly transmit via a vehicular wireless transmitter at least a second portion of the inputs to a remote tele-driving processor located externally to the vehicle; to wirelessly receive via a vehicular wireless receiver from the remote tele-driving processor, a remotely-computed processing result that is received from a remote Artificial Intelligence (AI) unit; and to implement a vehicular operating command based on the remotely-computed processing result, via an autonomous driving unit of the vehicle or via a tele-driving unit of the vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority and benefit from United Statespatent application number U.S. 62/644,510, filed on Mar. 18, 2018, whichis hereby incorporated by reference in its entirety.

FIELD

The invention relates to the field of teleoperation of vehicles orremote driving, as well as to autonomous vehicles and self-drivingvehicles (land, air, sea), artificial intelligence, communication withvehicles, wireless communication, and cellular communication.

SUMMARY

Some embodiments of the present invention include devices, systems, andmethods of autonomous driving and tele-operated vehicles. For example, avehicular Artificial Intelligence (AI) unit is configured: to receiveinputs from a plurality of vehicular sensors of a vehicle; to locallyprocess within the vehicle at least a first portion of the inputs; towirelessly transmit via a vehicular wireless transmitter at least asecond portion of the inputs to a remote tele-driving processor locatedexternally to the vehicle; to wirelessly receive via a vehicularwireless receiver from the remote tele-driving processor, aremotely-computed processing result that is received from a remoteArtificial Intelligence (AI) unit; and to implement a vehicularoperating command based on the remotely-computed processing result, viaan autonomous driving unit of the vehicle or via a tele-driving unit ofthe vehicle.

The present invention may provide other and/or additional benefits oradvantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system, in accordance with somedemonstrative embodiments of the present invention.

FIG. 2 is a schematic illustration of another system, in accordance withsome demonstrative embodiments of the present invention.

FIG. 3 is a flow-chart of a method in accordance with some demonstrativeembodiments of the present invention.

FIG. 4 is a block-diagram illustration of a sub-system, in accordancewith some demonstrative embodiments of the present invention.

DETAILED DESCRIPTION OF SOME DEMONSTRATIVE EMBODIMENTS OF THE PRESENTINVENTION

Reference is made to FIG. 1, which is a schematic illustration of asystem 100, in accordance with some demonstrative embodiments of thepresent invention. System 100 may include a primary vehicle 110 (whichmay be a drone or other machine); one or more other vehicles orsurrounding vehicles or nearby vehicles, which may be referred to as“secondary” vehicles, such as a secondary vehicle 150; one or moreinfrastructure elements 160 (e.g., a traffic light; a traffic sign; abus stop; a road sign; a sidewall device; a road-embedded device; amobile network infrastructure device; an Access Point or Hot Spot or HotPoint device; an antenna, a Radio Frequency (RF) transceiver or modem orrelay unit or amplifier or repeater; a cellular antenna or tower oreNodeB or base station) and particularly, an infrastructure element thatis configured or able to communicate wirelessly with vehicles and/orwith other local entities and/or remote entities); and a remote serveror remote node or edge computing/edge cloud node 170.

Primary vehicle 110 may comprise one or more sensors 111 which may be ofone or more types and models, for example, imagers, cameras,microphones, image acquisition units, video acquisition units, distancedetectors, LIDARs, proximity sensors, RADARs or the like; which are ableto continually and/or intermittently sense the surrounding of thevehicle. A vehicular storage unit 112 (which may optionally bedistributed) and stores data, raw data and/or partially-processed dataand/or processed data, that is captured or acquired by the vehicularsensors 111, and/or data that was captured by such sensor(s) and wasthen locally processed (partially or fully) by a vehicular dataprocessor 113 (which may optionally be distributed).

It is noted that the various vehicular units or modules that aredescribed above or herein, may be implemented in a variety of ways; forexample, as a roof-top sub-system having a roof-top housing or capsuleor encapsulation, such that units or modules (or at least some of them)are located physically on top of the roof of the vehicle; and/or as anin-vehicle sub-system such that units or modules (or at least some ofthem) are located physically within the vehicle, within the passengercabin, or near the motor or engine, or in the trunk of the vehicle;and/or under the vehicle; and/or as an accessory which may be plugged-into the vehicle; and/or as a distributed sub-system whose units ormodules are distributed across multiple areas of the same vehicle (e.g.,on the roof; under the hood; under the vehicle; near the motor orengine; in the trunk; in the passengers cabin; embedded in the vehiculardashboard; or the like).

Primary vehicle 110 may further comprise a human driving unit 123, whichmay enable a human driver or a human in-vehicle user to drive and/oroperate and/or control the vehicle 110; for example, via a Human-MachineInterface (HMI) or via a driving interface or a vehicle controlinterface (e.g., steering wheel, acceleration pedal, brake pedal, gearshifting stick or buttons or interface, a vehicular dashboard or screenindicating the current speed and/or direction of the vehicle and/or therounds-per-minute of the motor, signaling UI elements, or the like; oran HMI that utilizes other components, such as, joystick, mouse,trackball, touch-screen, touch-pad, screen, pedals, steering wheel,wearable gear or head-gear or goggles or glasses or helmet, tactileelements, haptic elements, or the like); which in turn may directly orindirectly control or trigger or command or actuate or engage with thevarious Vehicular Operation Units 124, such as, motor or engine,steering sub-system, acceleration sub-system, brakes or brakingsub-system, gears sub-system, electric sub-system, and/or otherparticular units of the vehicle, such as, tubes or pipes that transfergas or fuel, pistons, valves, ignition elements, vehicular actuation andcontrol components, switches, controllers, a vehicular drivingprocessor, or the like.

Primary vehicle 110 may further comprise, optionally, an autonomousdriving unit 120; which may analyze the sensed data, and may generatedriving commands and cause their execution based on analysis of thesensed data. For example, the autonomous driving unit 120 may determinethat another vehicle is located in front of the primary vehicle 110, andthat the primary vehicle 110 is expected to hit the other vehicle in 6seconds unless the primary vehicle slows down; and the autonomousdriving unit 120 may thus command accordingly the other units of theprimary vehicle 110, such as the Vehicular Operation Units 124 and/orthe engine and/or the motor and/or the brakes or any actuator orprocessor, e.g., to reduce or to stop the amount of gas that is suppliedto the engine (e.g., similar to a human driver releasing the gas pedalpartially or entirely), and/or to engage the brakes and/or to increase aforce of a braking process (e.g., similar to a human driver starting orcontinuing to push the brake pedal) or to reduce or cut the electricalpower supply for an electrical or hybrid engine vehicle. Theseoperations may be determined or commanded by a vehicular autonomousdriving processor 121; and may be executed or initiated or performed ortriggered by a vehicular autonomous driving mechanism 122 (e.g.,implemented using mechanical components, levers, pistons, pumps, tubes,mechanical elements able to open or close an opening of a gas tube,mechanical elements able to push or release a lever or a pedal,mechanical elements able to rotate a steering mechanism similar to theresult of a human driver rotating a steering wheel, electromechanicalunits or electronics such as processors, power suppliers, powerswitches, power converters, power distributors, or the like).

Additionally or alternatively, the primary vehicle may comprise atele-driving unit 130 or a remote-driving unit, or a tele-operation orremote-operation unit, which may enable a remote operator (e.g., human,or computerized, or AI-based) to remotely drive or to remotely operatethe primary vehicle 110 via wireless communication of driving commandsand/or vehicular operation commands from a remote transmitter to theprimary vehicle 110 or otherwise remotely intervene in its operation.For example, one or more vehicular transceiver(s) 132 (e.g., cellulartransceiver, Wi-Fi transceiver) which may be located in the or at ornear the tele-driving unit 130, and/or other vehicular transceivers 148or in-vehicle transceivers (e.g., which may be part of the vehicle 110but need not necessarily be part of the self-driving unit 130), maytransmit or upload data, and particular data sensed by the vehicularsensors 111 and/or data that was processed (partially or fully) by thevehicular data processor 113, to a remote recipient such as remoteserver 170. It is noted that each one of transceivers 148 and/ortransceivers 132, may comprise or may be coupled to or may include ormay be associated with, one or more antennas; and similarly, any othertransceiver that is part of system 100 may be coupled to or may includeor may be associated with one or more antennas; and such antennas may beinternal and/or external and/or co-located, and/or may optionally beshared by multiple or co-located transceivers in the same device, andsuch antennas are not shown in FIG. 1 in order to avoid over-crowding ofthe drawing.

A human tele-operator may engage via a tele-operation terminal 171 orother computing device with the remote server 170, and may generatevehicular driving or vehicular operation commands, remotely andexternally relative to the primate vehicle 110. The tele-operationterminal 171 may comprise, or may be coupled to or associated with, aHuman-Machine Interface (HMI), which may include, for example, touchscreen, screen, joystick, touch-pad, computer mouse, steering wheel,pedals, gear shift device, microphone, speakers, Augmented Reality (AR)or Virtual Reality (VR) equipment (e.g., wearable device, helmet,headgear, glasses, googles, gloves, or the like), haptic elements,tactile elements, gloves, other wearable elements, lights, alarms, orthe like.

Additionally or alternatively, a remote tele-operation processor 172,which may be part of remote server 170 or may be in communication withremote server 170, may process the data received from the primaryvehicle 110, and may generate vehicular driving or vehicular operationcommands, or driving guidelines or instructions, or location relatedcommands, or waypoints to the vehicular AI system, orapproval/confirmation (or conversely, rejection or cancellation) to anAI-planned route with or without changes to it, remotely and externallyrelative to the primate vehicle 110. The remote tele-operator, whetherhuman or computerized, may take into account other data that was notnecessarily sensed or received from the primary vehicle 110; forexample, data sensed and/or received from other vehicles, data sensed orreceived from infrastructure elements (e.g., taking into account that atraffic light is about to change from green to red in five seconds fromnow at 40 meters ahead of the primary vehicle), weather data, seismicdata, vehicular traffic data, or the like; and such data may be obtainedor received via a data fetching unit 173, which may be part of remoteserver or may be associated with it or controlled by it. Optionally, theTele-Operations Processor 172 of the remote server 170, may comprise, ormay be associated with, an Engagement/Disengagement Unit 193, which maybe responsible for reaching the decision whether to engage or disengagethe remote operation or the tele-operation, or whether to allow thevehicle 110 to proceed without forced remotely-generated tele-operationsor commands, or whether to over-ride the in-vehicle autonomous drivingunit 120 of the vehicle 110, or whether to otherwise remotely cause, ortrigger, or activate, or deactivate, or start, or stop, an Engagementprocess or a Disengagement process, relative to the primary vehicle 110.

The remotely-generated driving commands are transmitted from the remoteserver 170 (or from a transmission unit associated with it or controlledby it), directly or indirectly, to the vehicular transceiver(s) 132. Avehicular tele-driving processor 133 analyzes the incoming or receivedsignals or messages of tele-operation commands, and optionally convertsor translates them into locally-actionable commands that the vehicularsystem and the various Vehicular Operation Units 124 can then execute,if they are not delivered in this direct format. Optionally, suchtranslation or conversion may be performed by a dedicated component or asecondary processor, such as a Commands Translator/Compiler/Executor125, which may be implemented as part of the tele-driving unit 130, ormay be external to or coupled to or associated with the tele-drivingunit 130, or which may be an in-vehicle component that translates orconverts or interprets commands or signals between the tele-driving unit130 and the Vehicular Operation Units 124 that need to actually beactuated or activated or de-activated or configure or modified in orderto realize the actual command. The Commands Translator/Compiler/Executor125 may also convert or translate a single remote command or a singletele-driving command, into a set of particular in-vehicle commands thatcontrol or modify the operation of the various Vehicular Operation Units124. The Commands Translator/Compiler/Executor 125 may also convert ortranslate a set or batch or series of remote commands or tele-drivingcommands, into a single particular in-vehicle command (or, into adifferent, converted, set of multiple in-vehicle commands) that controlor modify the operation of the various Vehicular Operation Units 124.The Commands Translator/Compiler/Executor 125 may optionally comprise,or may utilize, or may be associated with, or may be implemented with orby, a Controller Area Network (CAN) or a CAN bus, optionally via theCANBUS protocol or other suitable protocol, that allows variousmicrocontrollers and Vehicular Operation Units 124 to communicate witheach other and/or to exchange commands and/or data and/or signals witheach other.

If the received tele-operation commands are “meta-commands” (or,commands that are generic in their nature or that are provided in aformat that any vehicle can interpret, such as, “come to a complete stopwithin two seconds” or “accelerate right now to 60 mph”), or commandsthat may generate or initiate a sequence of operations in the vehicle,such as AI-based driving instructions, then the vehicular tele-drivingprocessor 133 may distribute them to the relevant units or processor(s)(if not co-implemented) and such other units or processors then generatethe actual driving and actuating commands to the vehicular mechanicalsystems or units. The vehicular tele-driving processor 133 may thuscommand accordingly the other units of the primary vehicle 110, such asthe engine and/or the brakes, as described above. These operations maybe executed or initiated or performed or triggered by a vehiculartele-driving mechanism 134 (e.g., implemented using mechanicalcomponents, levers, pistons, pumps, tubes, mechanical elements able toopen or close an opening of a gas tube, mechanical elements able to pushor release a lever or a pedal, mechanical elements able to rotate asteering mechanism similar to the result of a human driver rotating asteering wheel, electric power handling elements or electronics orprocessors, or the like).

In some embodiments, the vehicular autonomous driving processor 121 andthe vehicular tele-driving processor 133 may be implemented using asingle processor, or as a unified component, or as a set of componentsthat share at least some of the logic. In some embodiments, thevehicular autonomous driving mechanism 122 and the vehiculartele-driving mechanism 134 may be implemented using a single mechanicalsystem, or as a unified mechanical system, or as a set of componentsthat share at least some unified elements or functions.

Primary vehicle 110 may further comprise an Engagement/DisengagementUnit 114, which may be responsible of engaging and/or disengaging one ormore of the above-mentioned functionalities, and particularly, to engageor disengage the autonomous driving of the vehicle by the autonomousdriving unit 120, and/or to engage or disengage the tele-driving or thetele-operation of the vehicle via the tele-driving unit 130, and/or toforce the vehicle into being controlled manually and exclusively by ahuman driver or a human passenger located within the vehicle, and/or toforce the vehicle to be controlled exclusively via the vehicularautonomous driving unit 120, and/or to force the vehicle to becontrolled exclusively via the tele-driving unit 130 based on incomingcommands, and/or to force the vehicle to be controlled by two or moremeans of control with a pre-defined order of priority (e.g., the vehiclewould perform the tele-driving command unless it is in conflict with alocal autonomous driving command which would thus prevail and would beexecuted, and/or to cut the power to an electric-power vehicle and/or tootherwise initiate an immediate stopping or stalling procedure in anyvehicle). The Engagement/Disengagement Unit 114 may operate based onlocal analysis of the sensed data and/or of other data that is knownwithin the primary vehicle (including, in some embodiments, based ondata that the primary vehicle 110 received from external sources, suchas weather conditions, seismic condition, volcanic conditions, trafficconditions, information from other vehicles or roadside infrastructure,or the like). Additionally or alternatively, theEngagement/Disengagement Unit 114 may operate based on a remotetele-operation command or input, or based on a locally-generated commandor input that was generated by the autonomous driving unit 120. Forexample, the remote server 170 (which may be, or may include, a node oredge node) may remotely command the Engagement/Disengagement Unit 114 toperform one or more of the above-mentioned engagement/disengagementoperations, based on input from a remote tele-operator (human orcomputerized); or, the autonomous driving unit 120 may locally commandthe Engagement/Disengagement Unit 114 to perform one or more of theabove-mentioned engagement/disengagement operations.

In accordance with the present invention, each one of one or moreinfrastructure elements 160 may comprise one or more sensors 161 (e.g.,from cameras, microphones, sensors, Light Detection and Ranging (LIDAR)sensors, RADARSs or RADAR sensors, or the like), one or moretransceiver(s) 162 (e.g., cellular transceiver, Wi-Fi transceiver), andan AI module 169 (e.g., implemented using a processor and a memoryunit).

Similarly, secondary vehicle 150 may comprise one or more sensors 151(e.g., from cameras, microphones, sensors, Light Detection and Ranging(LIDAR) sensors, RADARs or the like), one or more transceiver(s) 152(e.g., cellular transceiver, Wi-Fi transceiver), and an AI module 159(e.g., implemented using a processor and a memory unit).

Additionally, primary vehicle 110 may comprise an AI module 119; and theremote server 170 may comprise or may control, or may be associatedwith, a remote AI module 179.

In accordance with the present invention, data is sensed by the multiplesensors, such as vehicular sensors 111 of primary vehicle 110, and/orvehicular sensors 151 of secondary vehicle 150, and/or sensors 161 ofinfrastructure elements 161. The sensed data is exchanged, transmittedand/or received among entities in the system via transceivers (132, 152,162, 174); including sensed data, raw data, partially-processed data,fully processed data, data from external sources, and/or commands orsignals that are generated based on analysis of data. In accordance withthe present invention, each one of the sensors (111, 151, 161) mayprovide sensed data that may be utilized by the same entity in which thesensor is located, and/or by other entities in the system; andsimilarly, each AI module (119, 159, 169, 179) may contribute processingpower and AI resources in order to reach the most suitable decision withregard to engagement/disengagement and/or with regard to tele-operationof the primary vehicle 110 and/or with regard to autonomous driving ofthe primary vehicle.

In some embodiments, remote server 170 may be implemented as a “cloudcomputing” server or element; or may be located in relative proximity tothe primary vehicle 110 in order to shorten the communication time thatis required between the primary vehicle 110 and the remote server 170(e.g., optionally implemented as, or by using, an edge node or acellular edge node or a wireless edge node, or a cloud-based platform).For example, an Access Point (AP)/V2X Element 181 (e.g., comprising oneor more of: Wi-Fi Access Point or transceiver; Satellite-basedcommunication node or element or transceiver or terminal; DSRC node orelement; V2X node or element; or the like) may comprise an AI module 182which may similarly assist in AI analysis for the purpose of operatingthe primary vehicle 110. Similarly, a Cellular Infrastructure Element(CNE) 183 (e.g., eNodeB, or gNodeB, or cellular edge unit or tower orsite, cellular base station, fixed or non-moving cellular transceiver orstation or amplifier or repeater or relay node, or the like) maycomprise an AI module 184 which may similarly assist in AI analysis forthe purpose of operating the primary vehicle 110.

The Applicants have realized that autonomous vehicles and self-drivingvehicles are expected to become a reality and may even take overvehicles being driven by human drivers that physically sit at the wheelof the car or vehicle (or motorcycle, or train, or truck or bus or droneor helicopter or aircraft or ship or wheelchair or any other form ofmoving machine or other means of transportation by land, by air, or bysea).

The Applicants have realized that Artificial Intelligence (AI or A.I.)systems may be developed in order to allow a vehicle to be drivenautonomously by a computer residing in the vehicle and running this suchAI system or module. The Applicants have also realized that in somecases and countries or geographical locations it may be desired or evenmandated that a remote human driver shall be able to take over orotherwise assist or instruct the local (vehicular, or in-vehicle) AIdriving module or vehicular computer, for example, in emergencysituations, and/or in cases that are not learnt or not trained or notrecognized or not assured by the local (in-vehicle) AI system orvehicular computer.

The Applicants have also realized that in some scenarios, there may alsobe a human driver sitting in the vehicle ready to take local control ofthe vehicle as needed or if needed. This scenario, in which the AIself-driving module is stopping to function as the dynamic driver, maybe referred to as “disengagement” of the self-driving module or system.In some situations, such disengagement implies a partial or full failureof the local AI module(s) to perform the actual dynamic self-drivingdriving operations, momentary or during a prolonged time period or evenpermanently.

The Applicants have realized that such disengagement process may not besufficiently smooth or quick enough or just less so than possible, ormay be abrupt or may be problematic, for example, due to the local humandriver being negligent, or due to the human nature of falling intocomplacency or being bored by the passive monitoring task and thensuddenly being surprised into action at the spare of a moment out of avery bored and half-aware state, or since the human driver may benon-attentive or may be distracted or may be occupied with other tasks(e.g., utilizing his smartphone), or due to other reasons.

The Applicants have also realized that in such situations, interventionby a remote driver may be even more difficult as such remote driver(human or automated) or such human tele-operator or such remotetele-operator may not be able to actually see the surroundingcontinuously and at the same quality as the local onboard driver orsensors, as there may be a latency or delay in relaying the sensoryinformation or the sensed information (e.g., from cameras, microphones,sensors, Light Detection and Ranging (LIDAR) sensors, or the like) fromthe vehicle to the remote intervening driver or to a tele-operatingperson or module.

The Applicants have realized that there may be actual or potentialinstability or non-reliability of the wireless or cellular network linkor connection from and/or to the vehicle, and as a result, actual orpotential instability or non-reliability of the received sensoryinformation at the tele-operator, thereby making it difficult orsometimes impossible for the remote teleoperator to monitor and/or beready to intervene or handle remote driving (e.g., particularly if thetele-operator person or module may need to monitor and/or control morethan a single vehicle).

The Applicants have also realized that redundancy of systems, both ofhardware and of software and especially of AI components or modulesand/or of communications, may be desired or may even be mandated inorder to increase the safety and service level of autonomous drivingand/or self-driving systems and/or remote driving systems, especiallyfor reaching and supporting SAE Level 3, SAE Levels 4 and/or 5 (asdefined by the Society of Automotive Engineers).

The Applicants have further realized that AI systems may be developedwith an intention to serve only the vehicle in which they run. TheApplicants have realized that AI systems may be developed to alsobenefit other vehicles, other service operators, communication networksoperators, pedestrians, city operations, urban planners, or other usersor stakeholders of the roads or of the geographical region or of thedata being sensed and/or processed, in real time and/or offline and/orin retrospect.

The Applicants have realized that, for example, a vehicular AI modulewithin the vehicle may be communicating with an external and/or remoteAI layer or AI module(s) in a remote teleoperator's facility or a proxyor agent of such, may transmit to it only a pre-configured orpre-defined part or portion (e.g., per-condition type or portion) or anadaptive dynamic part or a learnt part of the sensory information asthis local vehicle that the vehicular AI module decides to do at everypoint in time, such as in view of its determined or estimated level ofsafety or assurance or prediction for the upcoming travel portion or theupcoming road segment or the availability or quality or cost of thewireless and/or cellular communication connection(s) connecting thevehicle with remote entities, or by taking into account other dynamic orstatic parameters and considerations and conditions.

For example, a vehicular Communications Properties Estimator 141 mayoperate to continuously or periodically estimate or re-calculate thecurrent or momentary properties of communications channels that areavailable to the primary vehicle 110 (e.g., bandwidth, throughput,good-put, error rate, packet error rate, rate of dropped or missingpackets, rate of erroneous packets, latency, delay, lag time, or otherdata and performance related parameters or the like); and/or may takeinto account recent or previous or past values of such properties (e.g.,in the past 10 or 30 or 60 or 120 seconds, or in the past 3 or 5 or 10minutes), and/or may take into account predicted values of suchproperties (e.g., predicted to be in the upcoming 30 or 60 or 120seconds, or in the upcoming 2 or 5 minutes; such as, by taking intoaccount the route that the vehicle is expected to travel and byobtaining an indication that in the next two minutes the vehicle isexpected to drive through a valley having low cellular reception).

A vehicular Data Packaging Unit 142 may take into account thecommunication properties that are estimated by the CommunicationsProperties Estimator 141; and may prepare a data-package to be sent ortransmitted or uploaded wirelessly from the primary vehicle 110 to theremote server 170 or to other wireless recipients (e.g., aninfrastructure element 160, a secondary vehicle 150, a nearby eNodeB orgNodeB, a cellular RAN element or a cellular Core element or an IP ISP(Internet Service Provider) or a nearby AP/V2X communication node 181,or the like). The Data Packaging Unit 142 may determine which portion(s)or part(s) or segment(s) of the vehicular sensed data (e.g., in rawformat or in partially-processed or processed format) to include in anoutgoing transmission, or to omit or discard.

Furthermore, a vehicular Dynamic Encoder/Modifier 143 may optionallyoperate to encode or transcode or re-encode or convert the data-segmentsor data-portions that are intended for transmission, from a first formator data-structure to a second format or data-structure, and particularlyto a reduced-size format or a compressed format; for example, byreducing a resolution of an image or a video-segment, and/or by reducinga color-depth of an image or a video-segment, and/or by modifying acompression ratio of an image or a video, or by changing an audiosegment from stereo to mono, or by changing bitrate or sampling rate orsampling frequency of audio stream(s) and/or video stream(s), and/or bydiscarding or omitting or diluting or skipping frames or packets orvideo-segments or audio-segments (e.g., discarding every Nth frame, orevery second frame, or every third frame; discarding every Nthmillisecond of audio-data or video-data or image-data), and/or byperforming other modifications.

The packaged data-segments may then be transmitted wirelessly by thevehicular transceiver(s) 132 of the primary vehicle 110 to one or moreother entities, as mentioned above; and the AI module(s) of such otherentities may operate to assist in the processing of such data in orderto provide to the primary vehicle 110 input and/or commands that wouldbe utilized by the autonomous driving unit 120 of the primary vehicleand/or by the tele-driving unit 130 of the primary vehicle and/or by theengagement/disengagement unit 114 and/or by other units of the primaryvehicle 110.

In some embodiments, the present invention includes devices, systems,and methods of remote driving and/or tele-operation of a vehicle or ofan autonomous vehicle, particularly using Artificial Intelligence andbroadband wireless connectivity such as using multiple-link vehicularcommunication, and/or optionally utilizing distributed AI resourceswhich may comprise at least one AI module that is external to thevehicle being driven and/or that is remote to the vehicular beingdriven, and/or with human intervention from a remote location (externalfrom the vehicle and remote relative to the vehicle) with or without theAI modules. The present invention may provide other and/or additionalbenefits or advantages.

The present invention includes devices, system and methods for addingredundancy, reliability and availability to remote teleoperation incommunications, AI functions and remote teleoperators—both human andAI-based, and teleoperators centers, while increasing the quality ofdata being shared from the vehicle and reducing the latency for remoteintervention, and further when adapting said mechanisms to unsupervisedongoing AI training that learns the and responds to various road andtraffic scenarios, various communication conditions and performances,various vehicular conditions, various remote teleoperators responses,network deployments and performances, or others; and may further enableto share and synchronize multiple remote teleoperation of same ordifferent vehicles. The present invention may further enable to matchgenerating, formatting, diluting, compressing or otherwise processing aswell as sending data from the vehicles and splitting them onto theavailable communication connections and combinations of them, takinginto account their cost and momentary performance characteristics.Furthermore, some embodiments may optimally or efficiently distribute AIprocessing tasks and elements through or via overlaying telecomnetworks, including 5G with numerous (e.g., hundreds or thousands) microcells deployments, so that relevant or optimal AI assistance can beexecuted at the network edge or close to it.

The present invention includes an apparatus, method and system for usingmulti layered or multi-components of Artificial Intelligence which maybe distributed onto several locations, one of which is at least onevehicle. These AI layers or components or modules communicate between oramong themselves using one or more adequate or suitable communicationlinks, at least some of which are wireless and/or cellular links of anysuitable type connecting the vehicular AI component to theinfrastructure, to a central remote AI component or layer, to other AIcomponents or layers running in the edge of the communication networkssuch as Access Points or eNodeBs (eNB) or others in the cellularinfrastructure RAN or Core or higher IP communication layers, to the AIcomponents or layers running within a core network (such as XUF or XPFor PCF or AMF or XCF or SMF or RAN or Core Network component or adedicated function or any other component in 3GPP cellular network) oraccess of the communication networks between the vehicular component andany other central or local component, to the AI components running inother vehicles or transportation means or pedestrians, or to any othercomponent or layer of the system.

The AI component or the AI module(s) (119, 159, 169, 179) may be basedon any suitable mechanism or algorithm or paradigm or method, including,for example: deep learning, machine learning, a Neural Network (NN),deep learning, supervised learning, unsupervised learning, reinforcedlearning, heuristic based or assisted decisions (sometimes not referredto as AI at all) or a combination of any of these or other decisionmaking methods, including human-involved decisions.

Optionally, an AI Resources Coordinator Unit 178 may be included insystem 100, for example, as part of remote server 170, or as astand-alone unit, or as a unit controlled by remote server 170, or as aunit external to primary vehicle 110, or as a unit that is remote fromprimary vehicle 110, or as a fixed non-moving unit, or alternately as avehicular or in-vehicle unit within primary vehicle 110. The AIResources Coordinator Unit 178 may control or modify the distribution ofAI tasks or resources among the available or the participating AIresources or AI modules. In a first example, the AI ResourcesCoordinator Unit 178 may receive indications that the primary vehicle110 is located in close proximity to (e.g., four meters away from) asmart wireless traffic sign which has an AI module; and the AI ResourcesCoordinator Unit 178 may indicate to the primary vehicle 110 to transmitover Wi-Fi a live High Definition video stream to the nearby smarttraffic sign, so that the AI module of that smart traffic sign wouldprocess the HD video stream in real-time or in near-real-time using thefast processor and abundant memory of that smart traffic sign. In asecond example, the AI Resources Coordinator Unit 178 may determine thata physically-nearby eNodeB or another cellular network element or edgecomputing processor is able to efficiently process data of LIDAR andproximity sensors of the primary vehicle, and may allocate to thateNodeB the task of that particular processing. The AI ResourcesCoordinator Unit 178 may further control which entity sends raw dataand/or processed results to which other entity or entities in system100, and may further control what the recipient entity should do withthe data or signals that it receives. In some embodiments, the AIResource Coordinator Unit 178 may be a learning machine, which decidesand controls according to the learnt information about the load,predicted load, estimated load, current AI task requested, locality andavailability of data relevant to the AI task requested, cost ofconnectivity and data exchanges, power consumption or other parameters,and/or any other correlation that it detects over time, which is usedfor determining or predicting the best or most efficient recipiententity for that particular data or signals or AI task.

In some embodiments, the vehicular AI module 119 or other componentexamines or analyzes the sensory or other data and determines theobjects around it, near or remote, static or in motion, at any X, Y, Zaxis and/or time, in any representation of them—sparse, core-set,categorized, classified, aggregated, full, partial, compressed, raw orother. The AI module may assign a weight to each such parameter,indicating the level of assurance or certainty that it has about thisobject or representation or parameter or set or subset of suchparameters. This certainty indicator or weight or score may be a singleindicator or it may be a set of indicators or coefficients for a set ofdifferent characteristics or features of the object. For example, oneassurance weight indicator may be assigned for the probability that oneobject is a car, or a truck, or a pedestrian or a lane; a secondassurance weight indicator may be assigned for the probability that itsX size (e.g., its length dimension) is as calculated, a third indicatormay be assigned for the probability that its Y size (e.g., its widthdimension) is as calculated, and so on for every characteristic orfeature(s) it may deem important for each such object. Identifying whichfeatures are important for each object at each scenario or point intime, including in view of other objects in the scene or absent from it,and including of the vehicular conditions and state and features anddynamic parameters, may be either known in advance, or heuristicallylearnt or adapted, or may be learnt or adapted via the mechanisms of theinvention AI, both at the vehicular AI level or at the remote AIcomponent or layer level, or any combination of them; or may bedependent on one or more conditions.

Once such a set of indicators, or a single summary indicator, ofprobability or assurance is (or are) calculated, the local in-vehicle AIcomponent such as AI module 119 may check and may determine whether ornot this calculated weight is below a pre-defined static thresholdvalue, or a dynamically-determined (dynamically modified) thresholdvalue in view of the scene or in view of the other conditions orcomponents or of the vehicle state. In such case, the vehicular AImodule 119 or AI component may decide to share this analysis, or part ofit, or the raw data that yielded it, or partially-processed data thatyielded it, or any representation of it, or any of the other relevantsensory or processed data, with other one or more AI layer(s) orcomponent(s) or module(s) which may be remote and/or external to thevehicle. Such decision to share may be taken in other processingalgorithms or methods as well; for example, in some implementations, itmay share all the sensory data, in uncompressed or raw format.

The decisions whether or not to share data with external entities forfurther processing, as well as which data or data-segments to shareand/or in which format(s) and/or via which communication link(s), may beperformed by a Data-Sharing Determination Unit 144. For example, suchsharing may be needed or advised or chosen for any suitable reason, forexample—for consultation with another AI component or layer or module,such that the system may have additional data or additional or strongercomputing power or may be powered by more data that was collected oranalyzed historically and therefore may have better assuranceindicator(s), or for purposes of obtaining a “second opinion” or even a“crowd-based” or “crowd-sources” opinion from one or more than a singleAI module that is remote and/or external to the vehicle, or as aprocedural step mandated by the vehicular or AI system vendor or by aregulator, or for any other suitable reason. In another type of cases,such sharing, of the same data or of a different set of data, may beselected or performed in order to assist in educating and teaching andtraining one or more other AI components in the system (e.g., externalto the vehicle; or located in a remote server or in a cloud computingservice; or located in other vehicles), for example, to allow them tomake real time decisions regarding other vehicles or pedestrians orother objects, and/or for offline training or learning to improveoverall performance and predictions in the future.

In some embodiments, uplink transmission or upstream transmission oruploading of data from the vehicle 110 towards any entity that isexternal to the vehicle 110, whether nearby (e.g., a nearby vehicle or anearby infrastructure element) or remote (e.g., a remote server or aremote AI unit or module or processor), may optionally be performed, atleast partially, over or via a virtual bonded communication link or abonded multi-link, which may be constructed and/or implemented and/ormanaged and/or configured and/or modified by a bonding unit 149; forexample, dynamically; based on, or taking into account, current and/ormomentary and/or estimated and/or predicted performance of eachtransceiver or modem that is available for such transmission or uploador up-stream, in order to dynamically determine and/or modify whichtransceivers are to perform the upload and which packets are todynamically allocate to each such transceiver for upload by it; or byselecting a pre-defined or pre-set combination of multiple transceiversand/or a packet allocation scheme. The bonding unit 149 may utilize ormay aggregate together multiple wireless communication links, andparticularly a bonded (or aggregated, or multi-link) virtualcommunication link comprised of multiple wireless IP communication linksserved by multiple vehicular transceivers or multiple in-vehicle orof-vehicle transceivers, or by multiple transceivers that are availablefor utilization by said vehicle but are not necessarily integratedwithin said vehicle (e.g., a transceiver of a smartphone or tablet of ahuman driver or a human non-driver passenger that is within thatvehicle); such that each transceiver is able to transmit or upload orstream, in parallel, some of the total packets that need to be uploadedto a certain IP destination address, and such that the recipient unitutilizes an assembly engine or re-assembly engine which re-assembles thepackets that are incoming from multiple wireless links into an orderedand correct data-stream. Optionally, the bonding unit 149 may define,determine, configure and/or modify, in a dynamic manner, whichcommunication link(s) to utilize and/or which transceivers to utilizefor such uplink transmission, thereby generating an ad hoc or acontinuous bonded channel or bonded multi-link connection; and/or whichpackets to allocate for uploading via each available link or transceiveror modem. In some embodiments, the data (e.g., sensed data that isuploaded from the vehicle, and/or processed or partially-processed datathat was prepared in the vehicle) may be transmitted via broadcast orunicast or multicast, or via a single one-to-one communication link, orvia a single point-to-point communication link, or via a one-to-manycommunication link or channel, or via a set or group or batch of severalone-to-one communication links or of several point-to-pointcommunication links; such as between (or among) the primary vehicle andany subset of the secondary vehicles and/or infrastructure elementsand/or the external or remote server or AI unit, in either direction(upstream and/or downstream; uplink and/or downlink; upload and/ordownload); using half-duplex or using full-duplex communication;optionally with an additional control channel or messaging channel orfeedback channel, or without such additional channel. Some embodimentsmay utilize IP connectivity without necessarily knowing the address of aspecific destination, or may allow sending of data from one sender tomultiple receivers or a group of receivers which may be listening-in onthe same IP network and without necessarily knowing in advance or at thetime of the transmission whether and/or how many recipient units areactually listening-in to such transmission or broadcast or multicast orupload or uplink Some of the transmitted information may be relevant tomore than one destination device (or recipient, or vehicle, or roaduser), and therefore some embodiments may utilize broadcasting and/ormulticasting as being more efficient and/or having lower delay indelivering the data to more than a threshold number of IP destinationsor recipients or information consumers or data processors.

When the AI module 119 or AI component decides or is ordered to share orupload or up-stream information or data with or towards another AIcomponent or any other entity, in some embodiments it may also decide byitself or via a Priority-Level Allocator 145 with regard to the prioritylevel of sharing each piece of information or data, or a set ofdata-items or a subset of the data, and/or it may decide with regard tothe format of the data to be shared or any other feature or property ofthe data sharing process. For example, it may decide that a camera feedfrom one of the front-facing cameras of the vehicles shows an objectthat its classifying or analysis or processing or identification oridentifying its movement vector or other property has one or moreassurance or probability indicators that are lower than the desired orrequired assurance probability level threshold in general or under thatspecific scene and circumstances, and that it is important to gainhigher assurance level or higher certainty level regarding this specificindicator or these specific indicators for this current scene or apredicted scene; and therefore it may assign to the sharing of thisinformation a high priority level, such as, for immediate sharing and/orfor sharing at a high level of detail and/or at high quality of data. Itmay also decide that the suitable format for this data to be shared isthe raw format as sensed and captured by the camera and/or with its fullfield of view, so that the remote AI component may have the highestpossible level of quality (e.g., maximum pixels or resolution) orsufficiently high enough quality and therefore accuracy for that full orwhole or complete view to base its decision on. It may also decide thatit has a high priority to send the previous 10 seconds of video fromthat camera (for the first time, or to send it again) in order toincrease the probability of the other (external) AI component or layerto reach a higher level of assurance indicators for that object orobjects or scene. It may decide that the set of these two types of datahas one combined importance indicator, but also that each of them alonehas a different importance indicator as there is a meaning of sendingjust the current feed, whereas in other cases it may be very lowpriority to send just the current feed without the previous seconds ofthe feed, or without additional different sensory raw or processed data.

Then, the AI module 119 or other component in the primary vehicle mayexamine all the data it decided preferable to transmit at that point intime, with their relevant importance weight. It may prioritize them alsoin accordance with the availability and quality of the communicationchannels or connections as it knows them or as it estimates them to beat that particular point in time. For example, a Data/Channel Allocator146 may operate to determine which data-segment or data-portion ordata-stream is allocated to be transmitted by which particulartransceiver(s) and/or communication link(s) and/or communicationchannel(s). For example, if information item A, being for example a highdefinition digital video stream compressed to a relatively high bit rateat delay X1 from the source due to the video compression process, andinformation item B, being for example a stream of sparse objectrepresentation of that video feed at X2 microseconds delay from thesource, and information item C, being a stream of sparse representationof the front LIDAR sensor at latency X3 from the source (e.g., due tothe AI process of classifying it and processing it), and informationitem D, being a sparse fusion of the side-angles LIDAR slice or angularimage (e.g., 90 degrees or 110 degrees or 45 degrees) and a side-facingcamera feed at a latency X4 from the source due to the processing time,and these four types of data were calculated to have the priority inwhich they were listed in the above example for that specific scene orin general to be transmitted to another remote AI agent or component orlayer or a teleoperator processor, then the vehicular AI agent or otherprocessor may also examine the available communication connection orconnections and their performance (e.g., latency, bandwidth, good-put,throughput, error rate, reliability indicator(s), or the like) at thatparticular point in time and potentially in predictive forward timewindow or in a forward time-interval. It may then decide, for example ifthe weights of transmitting stream A is not “high enough” over stream Band C, that instead of transmitting just stream A, because there is notenough bandwidth at sufficiently low latency and sufficiently highreliability from one or multiple communication connections bondedtogether or utilized in concert, that it prefers to transmit streams Band C instead, because it is more certain that they would arrive at theIP destination or destinations, because the impact of receiving bothstreams B and C is higher than receiving just stream A, or for bothreasons, or for any other suitable reason. It may then transmit stream Bover a bonded communication link or over a virtual connection consistingof two or more particular modems or transceivers (M1 and M4) and maytransmit stream C over a single modem or transceiver M2, leaving anothermodem or transceiver M3 unused for time-critical data orreliability-critical data at this point in time because its monitoredperformance is insufficient or inadequate for some or all of theinformation types. The next moment this may change, and the vehicularmodule may decide to use a different combination of modems and/ortransceivers (such as M2+M3+M4) for transmitting stream A and also maystill be able to transmit stream B over the combination of modems ortransceivers M 1+M2 (for example, modem or transceiver M2 is being usedin this example for transmitting both types of information).

In some embodiments, it may then decide whether or not it canaccommodate all the information to be transmitted within the capacityand the abilities of the current connections and their quality, in viewof estimations or calculations done by the Communications PropertiesEstimator 141. If not, it may consult with the AI module 119 whether adifferent format for some or all of the data is adequate or sufficient.For example, the AI module 119 may determine to more strongly compressthe current real time video feed and/or the feed from the previousseconds in higher compression, or to have and transmit a sparseprocessed representation of the images in the previous seconds, or totransmit the data by any other lower-quality transmission options inorder to accommodate transmitting of relevant high priority data overthe existing connection(s) and their current or estimated or predictedquality; and accordingly, the Dynamic Encoder/Modifier 143 may operateto perform such data compression or data dilution or data sizereduction. For example, some data may be defined as data that should ormust be sent in very low latency such that additional processing of suchdata may be irrelevant partially or completely; or that if such onemethod of processing takes lower latency and yet results in sufficientlow enough bandwidth to be accommodated within the available connectionsand their momentary or predicted performance, then such processing maystill be done.

In some embodiments, processing of the data may be done in parallel soas to save time and/or to reduce latency, according to the AI componentdecision or prediction of the future upcoming data and its expectedweighted assurance probabilities. The AI module 119 may commandadditional processing to start or stop accordingly to these learntpredictions, to be done in accordance with certain quality level(s) orset of parameters, to stop, to change the parameters for compression orencoding, or the like. For example, when the AI module 119 identifies anobject with a certain set or single assurance or probability indicatorwhich is too low, or a trend of reduced probability indicators overrelevant time or unstable or fluctuating in certain degrees (absolute orrelative) indicators over relevant time (derivative or anothermathematical method), it may decide and command a video encoder to starta new encoding scheme by using a second set of encoding parameter whichis intended for a lower yet “good enough” or sufficiently good videostream so that if the available communication performance is too low,this new lower quality stream may be selected for transmission insteadof the less compressed stream. Then, it may decide to stop this secondencoder, when for example the object or scene certainty indicator becomehigh enough and stable enough.

In another example, the AI module 119 may instruct a video processingcomponent to provide a sparse representation of the image or the videoin full or in areas of interest or of some specific objects, whereassuch representation may be generated by sets of pixels of differentnumbers, of classified objects, specific digital signatures, 2D or 3D,different resolutions, or other representations. Each representation mayhave an indicator associated with it, produced by the video processingor analytics or representation processor indicating the level ofsparseness or of assurance. The AI module 119 may learn or predict inadvance at high enough probability level that a certain video processingor sparse presentation is desired or sufficient for the specific scene,or object, or vector, or movement or any other criteria. may factor-inthe weighted probability of the classified or identified or predictedobject, or vector, or scene, the weighted probability of the importanceto arrive at a higher level probability for that item, the assurance orprobability of that item when the results of the video processing or AIprocessing of the video is combined with information from othersensors—either at the raw data level or at the processed data level orat the conclusions or prediction or probability level, the assuranceindicator of the other objects, the importance of that object to theoverall scene or to the tasks at hand such as of the dynamic driving orroad hazards or safety, or any other suitable or relevant information.

In some embodiments, with AI and machine learning, the exact decisionalgorithms by which a machine arrives at a conclusion may be unclearand/or irrelevant. Rather, the prediction is given with a certainindicator of the probability or certainty of its accuracy. The presentinvention uses this indicator of probability (or certainty) or otherthreshold or decision criteria or algorithm as a parameter foradditional machine learning or decision-making process(es), which mayoccur in parallel to the initial one, that determines which informationand at what quality and format, shall be transmitted and shared withwhich other AI component(s) or module(s) n the system. Then it mayselectively transmit the relevant information or data-segment, on or viathe relevant connections or channels or communication link(s) accordingto the priorities and while matching between information streams orbursts and each connection's actual momentary performance or predictedperformance.

All or some of these decisions may be made by the system based onpre-configured algorithms, and/or according to the AI component learningover time using machine learning methodologies, with or withoutheuristics, with or without local human intervention or remote humanintervention, in combination with the learning and/or results of thelearning and training of other AI components or layers of the system.

In some embodiments, the vehicular AI component or module, and/or theremote AI component or components or module(s), may adaptively learnwhich information types are relevant for decision making and/or have themost impact on remote tele-operation or remote tele-driving proceduresor assisted driving, or on how quickly and safely and most effectivelyoverall can a remote human or a remote AI tele-operator transit from itsidle mode to a full control of remotely driving the vehicle from aremote location. Similarly, the matching between communicationconnection(s) or a multiplicity of them with the information type orstream, and with its digital representation, format, quality, sparsity,and/or metadata, may have similar impacts on the effectiveness or safetyor latency or other parameter of a human or AI remote teleoperatortaking over a disengaging vehicular AI-based driving. The variousoptions are given or allocated weights, they are prioritized or ordered,and they are ranked so that the whole scale of options may be considered(or up to, or down to, a certain level). The importance, impact andeffectiveness of the transmission of the streams of data, and theirmatching to being transmitted over parametric communicationconnections—single or bonded or duplicated or others, may be learnt orgiven heuristically or a-priori configured also according to scenes, orscenes types, or other objects in the scenes or predictions related tothe scene.

For example, when the road is clear and there are no on-coming traffic,then a certain relatively-low level of assurance or probability orcertainty of an object in the road or on its side may be decided to besufficient and/or to have less importance, for example, compared to asituation when on the same road there are many oncoming or other trafficvehicles, and/or also depending on the vehicle or other vehicles orobjects speeds, movement vectors and directions, trajectories, and/orother conditions such as time of day, night-time, visibility level,weather conditions, presence of rain or snow or fog, ambient lightlevels, the speed at which the vehicle is currently travelling, and soforth. For example, in the no-traffic case, the vehicular AI componentmay decide to still keep transmitting a sparse or reduced-volume orreduced-size or diluted digital representation of that low-probabilityobject to a remote AI component and/or to a remote teleoperator ormultiplicity of such; whereas in the second case, when there is a lot oftraffic and/or there are a lot of objects to analyze, the object on theroad with the low probability identification or other feature may now beassigned a high importance and the vehicular AI component may decide tobond together available modems (or transceivers) M1 and M3 and transmitor upload a compressed video on or via this bonded virtual connection,while also streaming or uploading sparse representation ofLIDAR-captured data or other data on bonded modems (or transceivers) M2and M3. In some embodiments, the vehicular or other AI components maylearn to distinguish between such cases, to select the relevanttransmission options according to the information to be transmitted andthe options of transmitting other types of information or otherrepresentations of the same information or other formats of such. Suchlearning is made possible when at least some of the sensory informationis continuously transmitted to the remote destination so that the remotedestination teleoperator or AI component may check its predictionsaccording to the later stream data coming in, even if that is not a fullrepresentation of the raw sensory data.

The AI vehicular entity or component, such as the AI module 119, inaddition to its function as part of dynamic driving of the vehicle, mayalso decide what sensory information to transmit or upload and/or inwhich format and/or with which type of compression and encoding and/orover which modem(s) and transceiver(s), as well as deciding similarlywith regard to transmission or uploading of a partial subset of it, adiluted sparse representation of it, a post processed data from it, oneor more processed decisions based on it, or any other representation ofany part or subset or complete or fused part of the sensory collecteddata or of the decisions or predictions or metadata it calculated.

In some embodiments, a subset of the sensory data or the sensed data orthe measured data that is collected or captured or acquired by or in thevehicle may be shared with the remote processor or processors (or theremote AI modules) over time. This may be done with, or without, regardof the immediate assurance or probability or certainty indicator or thetrends of change in their quality or any other function of them. Thestart or stop or duration of such sharing may also be independent of theassurance indicators quality. The quality and format of the shared datamay also be independent of the assurance indicators quality. Each ofthese parameters may change dynamically over time, or may be static andpre-configured. The AI component or module in the vehicle and/or withinany other component in the network may learn and/or predict when tochange each of these parameters according to the results, theprobability level, the scene, the communication connections performance,geo-location, time of day, day of week (e.g., weekend versus weekday),cost of local processing or of remote processing or of communications,weather conditions, ambient light conditions, vehicular trafficcongestion conditions, visibility conditions, the task at hand, or usagetarget or other target function or optimization function, or any otherparameter that a supervised or an unsupervised machine learning may usefor this purpose.

For example, when a teleoperator (or remote driver, human or AI-based)may be used to potentially intervene with an autonomous vehicle drivingto take control of the dynamic driving in part or in full or to sendcommands or waypoints or other navigation or destination or drivinginstructions to it or approval/confirmation to an AI-planned route withor without changes to it (or, with modifications to it), at emergenciesor unlearnt or low probability situations, the latency of intervention,the accuracy of intervention, and the reduction of the “surprise” factorand/or of the non-alertness and non-conscious state of the remote driverin relation to the specific machine should be reduced. Especially whensuch remote driver may be supervising several such autonomous vehiclesand should be ready to intervene with any of them, or when the rate ofintervention caused by disengagement of the autonomous vehicle drivingsystem is low such that any such intervention comes at a surprise tosuch remote driver. The system thus uses at least one communicationconnection to transmit relevant presentation of the relevant sensorydata to minimize the latency at which such teleoperator may take overthe vehicle. Instead of the vehicle AI component starting to transmitthe relevant representation when the required moment of disengagementcomes, it transmits a potentially different set of representations ofpotentially different or same sensory of predictive information beforethe moment of disengagement and of remote intervention comes. Forexample, it may continuously assign a relatively high priority, but nottop priority, to a sparse or diluted or partial subset of theinformation or their vectors and therefore continuously transmit it overother less prioritized items. The remote component shall be able todisplay this sparse representation in a way sufficient for the operatorto be able to take over the dynamic driving of the vehicle at any chosenlevel of certainty or probability or clarity.

For example, the vehicular AI component may decide to transmit or toupload only a highly encoded (meaning strongly compressed, resulting inlow volume of streamed bitrate) video feed of the front camera. Sincethe required bandwidth is sufficiently low, it may transmit it over anycellular or V2X connection continuously. Still, it may be enough for theremote teleoperator to take over the vehicle dynamic driving at anypoint in time at the latency (delay) of the transmission from thedisengagement or of the event, rather than at the latency (delay) of thetransmission plus the latency (delay) of encoding or other processing ofthe sensory information. This transmitted information of sparserepresentation of the actual sensed surrounding or internals of thevehicle may be graphically presented to the teleoperator so he may feelas if he is the driver, at least to some extent, at any point in time.The details of a car coming from the other direction may be lessimportant than the actuality and “block”-size sparse digitalrepresentation or a highly compressed video of this car, so that theteleoperator may take over instantaneously and start sending drivingcommands from his remote driving set (wheel, brakes, gas and/or electricpower level data, or any other) at a minimal latency.

In some embodiments, the remote driver or remote AI module may send backto the in-vehicle AI module, over the single or bonded communicationlink(s) or channel(s), using unicast or multicast or broadcast over oneor more of the wireless connections, the driving instructions one afterthe other or in series, or batched or grouped together. For example, ifthe first way-point or destination is clearly identified, such as to theroad shoulders to avoid or remove a hazard, then this waypoint will besent quickly to the vehicle. Later points in the sequence, or out ofsequence, may be communicated or sent or transmitted later, together asa group or batched, or again one after the other. The waypoints ordriving instructions may be sent when the vehicle is stopped and/orduring its driving, either by the in-vehicle AI or by the remote driveror remote AI, or otherwise. In some embodiments, optionally, severaloptions may be sent to the vehicle, rather than a single deterministicset of commands; so that the in-vehicle AI may dynamically choose ateach point in time from that set of provided waypoints or otherinstructions, the most relevant next point or command according to thedeveloping or evolving or dynamic conditions. These multipleinstructions may or may not be prioritized by the remote driver orremote AI unit; may or may not be dependent on one another or onexternal conditions (e.g., using operators such as “if” and “then”, or“when” and “then”, and using Boolean operators such as AND and OR andNOT, or the like; such as, “if the brakes are responsive then brakeimmediately; if not then trigger the emergency hand-brake and lower thegear”). Optionally, a remote driver may interact with a remote AI andthen the resulting instructions may be sent to the in-vehicle AI module.In some embodiments, these operations may depend on the quality of thevideo uplink transmission, or the uplink transmission at large, becausea better quality video, for example from more cameras or at a higherframe rate per second or at higher resolution, may result in a higherconfidence level to perform from remote, or command the in-vehicle AI toperform, or may enable to initiate a higher risk maneuver; whereas alower quality communication or video or images may enable to system toenforce or advise only a more limited maneuver or driving commands. Thequality may be composed of the quality of the total of all the bondedmodems or transceivers, or any subset of each; and the qualityindicators may include the available bandwidth, goodput, through-put,latency, delays, error rate, rate of missing or lost packets, rate oferroneous packets, or the like.

In some embodiments the in-vehicle or remote AI may plan a route yetrequire a remote human teleoperator approval of it, with or withoutchanges to it. For example, if the AI-planned route involves law orregulation violation. Then the remote teleoperator may use the sensorsto gain understanding or confidence of the AI-planned route, approve itor make changes to it such as by moving a route line on a touch screenor marking new/delete/change way points or disapprove it altogether, orwait till conditions allow it and then issue the approval or otherwiseinteract with it and/or with the vehicle systems and/or with the AImodule. Using the bonded modems increase the quality of the sensoryinformation, hence increase the remote teleoperator confidence andunderstanding of the situation. HE may also direct the vehicle sensorssuch as cameras to better point at obstacles or areas of interest. TheAI that planned the route may direct the sensors in order to allow theremote teleoperator what it considers a best/optimal view, which may bedifferent than the view/angles/directions of the sensors when in motionor for other needs. For example, the AI module may point a side camerato look directly into a side object, or a side street or turn. Or one ofthe front-looking cameras to look sideways, even at a small anglechange, into a turn or a side street or a junction. Or to zoom on anobstacle, or escort the movement of obstacle such as pedestrians orothers.

In some embodiments, the vehicle is further equipped or associated withmultiple physical connections, or modems or transceivers. For example,it may be associated with three cellular modems that are operable via 1or 2 or 3 different operators. Each of these connections, or modems, maybe experiencing different performance at any point in time, for examplein terms of available uplink bandwidth, uplink latency, uplink errorrate, jittery behavior of any of them, downlink performances, bandwidth,goodput, throughput, general error late, or others. It may also beassociated with a V2X DSRC 802.11p modem or transceiver or connection,or more than one, or with another V2V modem or transceiver orconnection, or satellite modem or connection or transceivers, or others.

The vehicular component such as the AI module 119 may decide tocontinuously or as much as possible or needed, transmit a sparse ordiluted representation of said sensory information or results ofcalculations and processing of such, using the multiplicity of theselinks or connections. Such usage may be done for example in one of thefollowing ways.

(I) It may transmit the sparse representation via one selectedconnection. The selection may be done according to rigid parameters suchas performance, or according to an AI learning and training of the bestconnection to transmit on at any point in time and other conditions.

(II) It may transmit the sparse information replicated or duplicatedover a subset multiplicity of the connections. For example, in order toincrease reliability that every packet passes through, when there is nohigher priority information to be transmitted on any subset of theseconnections.

(III) It may transmit a different set of the information over multiplesubsets of the available connections at any point in time. For example,the strongly encoded video feed from a front camera split dynamicallybetween connections A and B and C bonded together into a single virtualconnection and the sparse block representation of objects from the righthand-side camera split over bonded connections B and D. Using whichconnections for which information may change dynamically by the system.When the performance is insufficient, or predicted to be insufficient,the component or AI module 119 may change the information it transmitsby dropping part of it, by commanding the processors to dilute or encodeif stronger (e.g., into fewer bits), by rescaling or resizing orsize-shrinking captured images and/or captured video, by skipping aframe of a video stream every K frames, by skipping every Nth frame of avideo stream, by using a lower bitrate for video encoding and/or audioencoding, by reducing sampling frequency, by utilizing an encoder or anencoding scheme that yields smaller data size, or to perform any otherchange.

(IV) It may transmit a more strongly encoded or diluted representationon a single connection A, and a mid-sparse representation of same ordifferent subset of the information on bonded connections B plus C, andthe least sparse, yet perhaps still sparse, representation of same ordifferent set of information, or predictions, on bonded connection Cplus D (C is intentionally not mutually exclusive in this example).

(V) It may transmit any subset of the information or the predictions orthe processed information or the representation of such to severaldifferent IP addresses using any combination of any subset of themultiplicity of connections. The selection of which connections are usedfor each transmission segment of which information may changedynamically, as well as the method of transmission (bonded, duplicated,single connection or other). In parallel to the transmission of saidinformation, on any subset of the used connections, other informationmay be transmitted or received; for example, infotainment orentertainment related packets, geo-location data, telemetry, mappinginformation, road traffic information, route guidance information, orother less important or lower priority packets.

(VI) It may transmit the same or different set of data or processed dataover each of the A and C connections, either to the same IP destinationor to different ones. It may select connections A and C because theseare the connections with the lowest momentary latency, or the highestbandwidth or lowest error rate or most stable or the lowest cost or anycombination of these or other parameters. It may transmit other types ofdata, related or non-related to each other, over these connections too.In subsequent moments, based on monitoring the performance of each ofthe links or per predicting their performance or otherwise, it maydecide to switch to connections A and D, or to add another connection Eover which to duplicate the same set of data or transmit a differentset, or any other change in the connections being used.

(VII) It may decide to transmit the sparse classification representationvia connection A bonded with connection B, and to also transmit a verylow-bit compressed representation of the side camera video overconnection C, and then later change the connection utilization accordingto their fluctuating or predicted fluctuating performances.

In some embodiments, it may transmit the information in unicast to anyspecific IP destination or address or recipient, or may multicast it orbroadcast it. For example, when it wants to share sparse information ornot sparse information with AI components in its vicinity and/or withinfrastructure and/or transmit them to a multiplicity of potentialteleoperators that may potentially intervene in case of vehicular AIdisengagement, it may broadcast it over the 802.11p or another flavor orversion of DSRC or V2X connection in parallel to unicasting it to aspecific IP address of a specific remote teleoperator.

In some embodiments, there may be multiple remote teleoperators and/ormultiple remote AI components or hierarchies or layers. For example, insome embodiments, the vehicular AI decides to transmit to two differentteleoperators using the same bonded virtual communication link using forexample connections A plus B plus C. This may be done using multicastand/or a Virtual Private Network (VPN); or, for example, this may bedone by replicating the transmitted data or parts over two or more ofthe available communication links or connections, separately unicastingit to the different destination IP addresses of the different receivingteleoperators. The two (or more) remote teleoperators may receive therelevant information at different performance characteristics, such asslightly lower or higher latency; for example, the first tele-operatormay receive the data at a first latency and at a first error rate;whereas the second tele-operator may receive the data at a second (e.g.,smaller) latency and/or at a second (e.g., smaller) error rate.Additionally or alternatively, one teleoperator may already be busyhandling other tasks or even remote-driving another disengaged vehicle.Any one of the said teleoperators may take over the disengaged vehicle.Alternatively, a remote AI module may take over from the humantele-operator, or vice versa, or the two may interact wherein the remoteAI advises the teleoperator about the alternatives or recommends optionsto it, or the human operator alleviates potential risks or limitationsso that the remote or in-vehicle AI unit(s) can then continue with theroute planning alternatives or recommendation or actual driving. Forthat purpose, synchronization or another type of coordination betweenthe teleoperators and/or between the teleoperators and the vehicular AIcomponent or vehicular processors and/or via another entity (e.g., acentral AI component or layer) may be done, for example, via timestamps,IDs, packet IDs, a control channel, a feedback channel, a feedback loop,codes, control messages, transmission and reception of acknowledgement(ACK) packets or codes, transmission and reception of NegativeAcknowledgement (NACK) packets or codes, collision-avoidance schemes,ordered list of priorities or prevailing rules, and/or other means. Thevehicular AI may identify which of the allowed remote teleoperatorbecomes its designated teleoperator, and may then discard or disregardcommands received from other one(s). In some embodiments, the vehicularAI module, or a central/remote/external AI module, may be the modulethat determines (in advance, or in real time during the disengagementprocess) which particular tele-operator, out of two or more possible orrelevant tele-operators, would take-over and perform the actualtele-operating of the vehicle upon disengagement. The decision may bereached by taking into account one or more parameters or conditions, forexample, current or historic or predicted or estimated or actual qualityof communication with each candidate tele-operator (e.g., latency,delays, error rate, Quality of Service (QoS), bandwidth, goodput,throughput, reliability of connection, or the like), the current orpredicted or estimated workload of each candidate tele-operator (e.g., atele-operator that is already remote-driving another vehicle, may bedefined as less preferable over another tele-operator or remote AI thatis not currently remote-driving another vehicle), a pre-defined order ofpriority (e.g., defining that if tele-operators A and B and C are allavailable, then the vehicle owner or the vehicle maker or the systemadministrator had defined that tele-operator B would be selected),and/or other suitable conditions or criteria or parameters. In someembodiments, potential teleoperators may exchange information in realtime to make this decision between or among themselves. In someembodiments, the teleoperator that takes control, using any decisionmaking process or default decision making process, may inform the othersand/or a central entity or location, the vehicular processors and/orother entities, using communications, human UI means, or others, and maycontinue interactions with such.

In some embodiments, the remote teleoperator or multiplicity of suchtele-operators may be or may comprise learning machines, or other AIcomponents or AI layer or AI hierarchy. Each one of them may becongested or occupied to a certain degree in terms of computational orconnectivity or cost or other resources. Further, each one of them maybe implementing a different method or algorithm or tuning parameters, ormay have a different experience or expertise (e.g., machine that isexpert with rainy conditions; machine that is expert with night-time orlow visibility conditions; machine that is expert with road hazards; orthe like), and therefore may have different training and decisionresults. Hence, each one of them may react differently to the situation;some may perform better in some situations, and other may perform betterin other situations. According to the transmitted and receivedinformation, and to their experience and expertise and level andcapacity, each AI component may rank itself as to how well it estimatesthat it can handle the situation. The vehicle AI component, or anotherAI component, or between them, a relevant AI component may be selectedor pre-selected by default, or by being the quickest to respond, for theremote teleoperation. For example, tele-operator A may indicate that itis particularly experienced or trained to remotely drive a vehicle inrain conditions; whereas tele-operator B may indicate that it isparticularly experienced or trained to remotely drive a vehicle atnight-time; whereas tele-operator C may indicate that it is particularlyexperienced or trained to remotely drive a vehicle that havemalfunctioning brakes; and the selection of the particulartele-operation may take into account which one of these (or other)parameters or conditions hold true for the particular vehicle thatrequires disengagement and remote-driving.

In some embodiments, the decision of which remote assistance is providedat each point in time may also depend on the quality of the connectionto or from the vehicle and or other vehicles in that vicinity. Forexample, if the uplink video quality of the bonded or single link (e.g.,in 5G communications) is limited or is worse in any respect, then aremote human tele-operator may be alerted and take control. Whereas, ifthe video or data uplink quality (e.g., taking into account latency,reliability, error rate, bandwidth, and/or other parameters) issupportive enough, a remote AI may take control. This may operate in atiered model; such that, for example, firstly the remote AI evaluates ifit can serve that instance, reviewing the conditions including thecommunications conditions as reported by the vehicle or as derived fromthe quality of the received data, and then, if needed, it hands over thecase to the remote human teleoperator. In case communications conditionschange, for example, the bandwidth drops below a threshold value or thevideo frames per second or resolution or details-in-the-image drop belowthreshold values, then the remote AI may alert the human tele-operatorand hand the case over (e.g., back to the AI module). In case thecommunications conditions improve, or the critical obstacle thatprevented the local or remote AI from handling case is removed or istraversed by the remote human tele-operator, then the situation may behanded over to the remote AI or to the in-vehicle AI. Using bondedcommunication of multiple links provide the reliability, video quality,bandwidth, low latency or stable low jitter behavior, more cameras, moreother sensors, or the like, which allows this economics of scale inhaving more remote AI handled cases and less humans in the loop, orimproved and more efficient (shorter and more reliable and effective)human handling.

The above-mentioned operations may be controlled or handled by aMultiple Tele-Operator Handling Unit 147, which may be a vehicular unitwithin the primary vehicle 110, or may be part of remote server 170 ormay be implemented externally to the primary vehicle 110. For example,the Multiple Tele-Operator Handling Unit 147 may determine and/orperform and/or instruct one or more of the above-mentioned operations,such as, which data-streams or data-sets or data-segments to send toeach one of multiple remote tele-operators; which remote tele-operatoris better suited or is best suited to handle which particular situationsor scenarios; which tele-operator or which tele-generated command wouldbe the prevailing one in case of a conflict or inconsistency or anomalyor abnormality or contradiction or mismatch or duplication amongdecisions or commands or among expected results or among derivedoperations from two or more tele-operators; and/or other suitabledeterminations or decisions, which may be performed based on apre-defined set of rules, or based on a pre-defined lookup table ofpriority order, or by based on the certainty level that each one of themultiple sources (of inconsistent commands) associates with its command(e.g., selecting the command that has the highest level of certaintyassociated with it), or based on other considerations that the vehicularAI unit may utilize for this purpose.

In some embodiment, a first layer of AI, the one that has for examplethe lowest latency and/or other local benefits, may take over thedisengaged vehicular dynamic driving first. For example, an AI componentthat is run by the telecom or mobile operator eNodeB or Wi-Fi hotspot—atthe edge. This AI component may provide “first aid” or “first response”actions, such as immediate handling of immediate accident hazard.Further, it may provide additional or other AI expertise and processingof the data being received by it, for example according to its pastlearnt experience and training. It may then “consult” or check with ahigher or different layer of AI component, or pass the additionalprocessing results or predictions and probabilities to the vehicular AIcomponent or to any other AI component being involved in this process.Such other AI component may be more adequate or capable to manage thesituation and in a synchronized or unsynchronized way take over and sendthe relevant instructions to the vehicular AI component or to thevehicular driving processors. Such learning may be done in anon-supervised machine or mode or technology, as it may continuouslycheck the received input against its decision and the new input cominglater, which may be used to validate or invalidate or enhance orotherwise positively or negatively reinforce the learning.

In some embodiments, the telecom or communication operators may run attheir edge nodes or in their core AI components overlay that interactwith each other. These AI nodes may receive information, raw orprocessed, from the vehicles within their physical geo-location coveragearea, from vehicles about to enter these areas, from the infrastructuresensors such as cameras on traffic lights or other sources. They maythen continually provide “AI escort” or “AI chaperon” to the vehiclesunder their service, in a priority order when resources are insufficientto manage or escort all. If AI component are implemented at the edgenode, such as Wi-Fi access point (AP) or eNodeBs of a cellular networkor other; then when a communication handover is performed, or soonbefore or after, also the vehicle's AI-state representation may behanded over from one such handling or escorting node to the other. Thismay occur when the vehicle is not teleoperated, as well as while it isteleoperated—either by the node AI component or by another AI componentin the system. This handover may be done via exchanging signals and/ordata and/or referrals to a data aggregator or to a central entity or acentral AI module. The geo-areas of coverage of an AI node may or maynot overlap in part or in full with the telecom or mobile-com coverageareas of the wireless nodes.

For example, when a vehicle moves from one AI node to another, it maynot necessarily need to switch communication connections or establishnew ones. However, when a vehicle undergoes a communication handover,its connections including with the AI agents or components may usuallybe maintained via the new telecom node or by both the old and new duringthe transition period. However, at these points, as well as at otherdifficult communication points such as cell edges or lower grade serviceor lower coverage or more users or other, lower performance might beexperienced for the delivery of the data to the remote teleoperator orremote AI agent or layer. In some embodiments of this invention, layeredAI paradigm may assist in that as the local communication node, or AInode, may provide first level of teleoperation or of AI processing. Theremote, potentially stronger or more capable or more experienced orlearnt AI component or human teleoperator may take control in asecondary manner, partially or in full.

In normal operation where the vehicle is self-driving and if the firstlayer of AI component monitors its thinned-down representation of thedriving, it may handover this to the next node in the expected or knowngeo/road route. It may identify potential hazards in advance and alertthe vehicular AI and/or the higher hierarchy AI component or agent,starting to transmit to it the data ahead of the predicted event thatmay or may not occur, request or instruct the vehicular AI agent toenhance the data delivery—raw or processed, to allocate more radio oraccess or QoS slices other communication resources or ask the relevantnetwork allocating elements to do promptly so that the additionalinformation from the vehicle may be transmitted safely and at a higherand more guaranteed QoS.

In some embodiments, having many multiple remote teleoperators,including potentially authorized and trained AI machines or humans oreven a crowd of humans, may involve a higher complexity of algorithmsfor deciding which takes over, or still the vehicular AI agent ordriving processor may decide according to which responded quicker or hasthe more bandwidth to/from or other communication results criteria orranking of the teleoperator himself according to capacity and pastperformance in general or relevant for this specific scene or case, orany combination of.

In some embodiments, AI agents or components or layer or any of the AImodule(s) may train itself or learn from the transmitted data from thevehicle. Such learning may be unsupervised as the ongoing stream of datashows current flow and then future (which becomes actual) flow, whichcan be compared with the prediction based on the current and past. Thiscan be done on an ongoing basis, or not necessarily just foremergencies, since the data stream keep flowing towards such AI modulesfor analysis. Optimization and target functions may be for objectpredictions, vector movement predictions, communication prediction,transmitted data efficiency for various AI decisions or targets such aswhen transmitting some types and qualities and formats of data and thenothers and learning the impact each transmission has on the quality ofthe results. Further, in some cases, several different types of dataand/or of qualities and/or of formats may be transmitted in parallel—ofthe same scene. They may be transmitted using the same communicationconnection(s), bonded or single or multiple, or over different ones withgenerally similar (or different) momentary performance characteristics.

When using bonding or aggregation of multiple communication links ormodems or transceivers, some of the transmissions may be of highquality/less compressed/less sparse representations of the same view ordata. Then, the remote AI component or layer or hierarchy may performcalculations on the different received streams, and may compare thequality of the results when compared to the later incoming actualstream. This method for on-going unsupervised training may expediate andimprove the quality of the AI components continually and quickly.

In some embodiments, the process of transmitting the information or thesparse part of it, the selection of which AI or human teleoperator takesover, what commands are being sent or other parts of the process, orother operations, may optionally use blockchain technology via aBlockchain Control Unit 177, in order to store or record data, totransact decisions (if not time pressured as current blockchain is notdesigned for microsecs real time contracts), to ensure data integrity,to make data (or part of it) available as a blockchain data-structure toAI modules and/or to machine learning modules and/or to auditing modulesand/or to regulators, to enable untampered logging of data and/or oftele-operating transactions, to authenticate participating components,to authenticate sensed data and/or processed data, to record share andprocess points-of-interest in the process, to democratize orcommercialize any part of the remote assisted driving or the autonomousdriving data processing or the data gathering for tele-operatingprocess, and/or for other purposes. Accordingly, a blockchain baseddata-structure constructor and updater may operate, in the vehicleand/or externally to the vehicle (e.g., as part of Blockchain ControlUnit 177), and may manage the construction and the subsequent updatingof such blockchain of data related to self-driving, remote-driving,tele-operation of vehicles, local or distributed AI data or decisions,and/or other related data or parameters.

The AI modules or components in the network or in the central AI modulemay sometimes experience more data traffic than the single vehicle does.Therefore, and/or due to other reasons such as resources availability,it may sometimes be able to better and/or more accurately respond tovarious particular scenarios. It may use the multilink connection withthe vehicular AI to download into it at a higher rate the most advancedor more updated AI training on-the-go or when it is parked or non-moving(e.g., waiting at a red light near a coffee shop that has a public Wi-Fiaccess point). It may use these downlinks, when not in use or whenprioritized higher, to further train the vehicular AI component. Thebonded multiple links may provide both the bandwidth, and thereliability and availability for such downloading or training to be themost effective and quickest, so that the vehicular AI may be the mostupdated in the quickest manner. Further, in some embodiments, anyauthorized AI component or agent in the layer may download or maytransfer into the vehicular AI agent the view, either physical orrepresentation of or processed decisions based on it, as experienced byother vehicles in the system such as the on-coming ones, or vehiclesgoing in the same direction but ahead of the vehicle, or pedestrians, orinfrastructure (e.g., traffic signal), or as processed by the other AIagents. The vehicular AI agent may use this downloaded or streamed orreceived data to reinforce its own decisions or to make new ones.

In some embodiments, an AI layer with multiple nodes may interact withthe vehicular AI agent and driving processors, and with the telecom ormobile communication network in order to best coordinate in advance orduring the process relevant resources for the remote teleoperation.

Some embodiments of the invention comprise devices, systems, and methodsof vehicular multi-layer autonomous driving, of remote tele-operating ofvehicles, and of self-driving and autonomous driving of vehicles,optionally utilizing distributed Artificial Intelligence (AI) and/ormultiple AI modules or distributed AI architecture. For example, avehicular (in-vehicle) unit comprises implements a first layer ofartificial intelligence, mainly but not solely, for autonomous driving.A second unit with a layer of artificial intelligence may optionallyexist and operate at the edge nodes of a communication networkinfrastructure, such as at the Access Points of a Wireless LAN (WLAN)network, or a eNodeB of a 4G or 4G-LTE or 5G or 3G cellular network, orat the core of such networks or at the access nodes of such networks.Another layer of artificial intelligence may optionally exist andoperate at remote computing devices such as in a cloud, in a command andcontrol center, at the home or others, including multiple suchlocations. The multiple AI layers communicate with each either,pre-process and post-process data that is fed or transmitted or receivedor exchanged or shared via one or more communication connection(s)between them, exchange decisions, suggest decisions, perform predictionsand estimations between them, for the safe continuous and cost effectivemovement and/or operation of the first vehicle and/or of others,providing layered hardware and software and operational redundancy forautonomous driving, thus increasing the safety, speed, efficiency andavailability of such autonomous driving, and with the delivery ofrelevant selected sensory data or sensed data such as diluted orcompressed video or AI representation of such, also allowing for aremote driver, or multiplicity of such, to take control of theautonomous vehicle instantaneously as the data (e.g., including video)is available to them at most times.

In some embodiments, a self-driving vehicle or an autonomous vehicle maycontinuously or intermittently upload, or transmit wirelessly in theuplink direction to one or more remote recipients, sensed data and/orcaptured data and/or acquired data (e.g., camera feed, audio feed,microphone feed, video feed, location information, vehicular speed,vehicular acceleration or deceleration data, LIDAR data, informationcollected or sensed by other sensors, or the like), on a generallycontinuous basis (yet sometimes with potential interruptions,disruptions, stoppages and resuming, or other errors or delays, whichmay be intended or not-intended), and optionally in a compressed orencoded format or in a small-size format or a diluted format (e.g.,skipping every Nth frame of a video) or in a reduced-size format (e.g.,resizing or cropping a larger image into a smaller image for uplinktransfer); thereby enabling a remote tele-operating system and/or to aremote tele-operating module or person to respond rapidly andefficiently to a disengagement event, and to shorten the Response Timeof such remote tele-operator (human and/or machine-based) since at leastsome of the information that is immediately relevant for taking over theoperating or driving of the vehicle is already available at the remotetele-operator location from such previous, ongoing, wireless uploadingof data.

In some embodiments, edge nodes or edge-based services or near-edge interms of IP-based connectivity (fewer hops, lower latency) or in termsof geolocation of cellular communication networks and/or cellularproviders and/or communication service providers, may follow or escortor chaperon the vehicle as it travels and may provide remote AI servicesto such traveling vehicle, and particularly as the vehicle traversesfrom one cellular communication cell to another cellular communicationcell; thereby ensuring a continuum of generally uninterrupted orlow-interrupted or reduced-interruptions AI support services to thetraveling vehicle; and such that a first edge node or cell may transfervehicular data or vehicle-related data to a nearby or subsequent edgenode or cell in order to ensure and enable such continuum of AIservices. In some embodiments, multiple such edge or near edge nodes maythus virtually chaperon or virtually escort the traveling vehicle,coordinated or not-coordinated among them, at all times, or whenappropriate according to risk analysis or learnt experience oravailability of resources, they may provide additional reliabilityredundancy coverage and AI power and experience for various conditionsand scenarios. This may further enable rapid response and rapidintervention by an AI module of such edge service or edge node, sincethe uploaded data packets need reach a remote recipient and/or need notneed to traverse long distances of the cellular network but rathermerely need to reach the nearby edge service node, enabling such edgeservice node to provide AI support and tele-operating service to thevehicle in a disengagement event, at least initially and/or on an urgentbasis.

In some embodiments, a particular vehicle or autonomous vehicle orself-driving vehicle, may be associated with two or more remotetele-operating systems, and may be able to communicate with them (or,with each one of them) via wireless/cellular communication link(s); andsuch two or more remote tele-operating systems (which may includemachine based vehicular operators and/or human vehicular operators) maybe defined as possible candidates to take-over the vehicle and toremote-drive or remote-operate or tele-operate it upon disengagement orif one or more pre-defined conditions hold true.

For example, a vehicular (in-vehicle) selector module, or anextra-vehicular selector module (e.g., located externally and remotelyto the vehicle, such as at a central server or a central communicationhub or a central AI module) may perform the selection or determinationof which particular candidate tele-operating system, out of two or moresuch candidates, would actually be selected and would be assigned orallocated the task of tele-operating when needed or when desired to doso. The selector module may take into account a set of parameters,conditions and/or criteria for this purpose; for example, the current oractual or predicted or estimated or historic characteristics of thecommunication between the particular vehicle and each such candidate(e.g., communication bandwidth, throughput, goodput, latency, delay,error rate, reliability, signal strength, Quality of Service (QoS) oftransmission and/or reception), current weather conditions andenvironment conditions (e.g., a particular tele-operator may havespecific experience to operate a vehicle traveling in snow or in fog orin rain), current geo-spatial parameters (current location, speed,acceleration), current time (e.g., a particular tele-operator may havespecific experience to support vehicles at night-time, or duringrush-hour), current day-of-week or date (e.g., a particulartele-operator may have increased resources or reduced resources duringweekends, or during holidays), road data (e.g., a particulartele-operator may have or may lack specific experience for remotelyoperating a vehicle in a mountain road or in an urban area or in aserpentine road), the current or estimated work-load or computationalload at each such candidate tele-operator, the number of other vehiclesthat are currently being tele-operated or monitored by each candidatetele-operator, the current or recent response time(s) exhibited by eachsuch candidate tele-operator, a pre-defined priority order oftele-operators that was pre-defined by an owner of the vehicle and/or bya maker of the vehicle or by a system administrator, and/or based on aweighted formula that takes into account a weighted combination of someof the above-mentioned parameters.

In some embodiments, such selection may be done passively orsemi-passively, for example, by selecting and/or utilizing the firstauthorized teleoperator from which the commands arrive at the vehicularprocessors. Optionally, teleoperation control may be handed-over fromone entity or remote controller to another, human and/or AI-based,according to decisions based on the listed parameters or others, overthe course of the operation. Human intervention may be applied to impactthis process in real time or in advance. Such handing-over of thetele-operating may be synchronized or defaulted, or may be performedautomatically if one or more conditions hold true (e.g., automatichand-over of the tele-operating from tele-operator A to tele-operator Bif night-time is reached, or if it starts raining or snowing, or if thevehicle moved from geo-region C to geo-region D, or if the computationalor other burden at teleoperation A reaches a pre-defined threshold valueor a maximum allowed level, or the like).

In some embodiments, a vehicular computer or vehicular processor or thevehicular AI module 119 may utilize AI and machine learning not only forpurposes of identifying objects and route-related items (e.g., othercars, traffic lights, traffic signs, lane borders, pedestrians, roadblocks, road obstacles, or the like); but also in order to learn whichtypes of data to transmit or upload to remote AI modules, and/or whichcompression or encoding or data dilution or sparse representationprocess to utilize, and/or to which extent to dilute or to encode or tocompress or to skip data or data-items, and/or to determine which dataoriginators (e.g., which sensors or cameras or LIDARs) to include and/orto exclude from such data transmissions or data uploading, and/or whichamount of data and type(s) of data are sufficient to upload or totransmit in order to enable a remote AI module to reach decisions at apre-defined or de-facto level of certainty or probability or assurance,and/or which one or more communication link(s) and one or more modems ortransceivers to utilize for such uploading or transmitting (e.g., fromone or more available modems or transceivers, including for exampleWi-Fi, satellite, cellular, 3G, 4G, 4G-LTE, 5G, cellular, V2X, or thelike), and/or whether or not to transmit or to upload a particulardata-item or data-stream over two (or more) different communicationlinks and/or to two (or more) different recipients, and/or whether todivide and in which particular manner to divide packets that areintended for uploading across two or more modems or transceivers orcommunication connections which then upload or transmit them in concertor in parallel at different rates and/or at different performancecharacteristics (error rate, latency, delays, bandwidth, goodput,throughput, QoS parameters, or the like), and/or othercommunication-related decisions or determinations, that can bedeep-learned over time and/or trained by an AI module, while alsochecking and learning whether a particular set of parameters and/ordecision is sufficient to enable sufficient AI determinations beyond apre-defined threshold level of certainty or probability or assurance.Such AI process may utilize actual data, predicted data, estimated data,raw data, processed data, diluted data, historic data,currently-measured or currently-sensed data, recently-measured orrecently-sensed data, and/or a suitable combination of such data types.

In some embodiments, a local in-vehicle AI module 119, and/or a remote(external to vehicle) AI module (179, 169, 159), may continuously learnand machine-learn and improve its identification of objects and improveits decision making, by continuously analyzing the incoming data-streamsthat are sensed and/or transmitted or uploaded by the vehicle or fromthe vehicle, and then comparing the decision results or theidentification results to newly-incoming or fresh data that is receivedin a continuous manner only 1 or 3 or 6 or 10 or N seconds later. Forexample, a local or in-vehicle AI module, or a remote AI module, maycontinuously receive a compressed or diluted stream of front-side cameravideo stream from a vehicle traveling on a road; may analyze 28 imageframes that were received over a period of three seconds; may determinethat there is an object located ahead on the road, and that the objectis either a static oil stain (at 80 percent probability) or a moving cat(at 20 percent probability); then, five seconds later, as the vehicleapproach the object, the front-side (or left-side) camera of the vehicletransmits closer images of the object, which allow the AI module todetermine at 99 percent certainty that the object is indeed a static oilstain and not a cat, thereby training and learning for subsequentdetections to better identify an oil stain or a cat or to betterdistinguish among objects.

Some embodiments of the invention may operate in conjunction withvarious types of a generally self-driving car or vehicle, autonomous caror vehicle, permanently or non-permanently driverless car or vehicle,un-manned car or vehicle or UAV, robotic car or vehicle, robot-based caror vehicle, smart car or smart vehicle, connected car or connectedvehicle, specialty car or specialty vehicle, or other types ofground-based or air-based or sea-based vehicle that is capable ofsensing its environment and operating (e.g., driving, flying, sailing,moving, navigating) without human input and/or that is capable ofcomputer assisted driving and/or remote computer assisted driving and/orcomputer assisted operating and/or remote computer assisted operatingand/or tele-operating and/or tele-driving with or without remote humanintervention of any sort or local human intervention such as taking overcontrol of the vehicle upon emergencies or unknown or low-certainty orprobability situation or in pre-designed situations such as the “lastmile” or off-highways or when approaching landing for UAV or within theport for unmanned ship, or upon any other need or desire.

Such vehicles may be referred to herein as “autonomous vehicle”, and mayutilize a variety of sensors and detectors in order to sense, detectand/or identify their surroundings and/or other data (e.g., trafficlights, traffic signs, lanes, road blocks, road obstacles, navigationpaths, pedestrians); for example, cameras, image camera, video cameras,acoustic microphones, audio capturing devices, image and video capturingdevices, radar sensors or devices, LIDAR sensors or devices, laser-basedsensors or devices, magnetic or magnet-based sensors and devices,ultrasonic sensors, night-vision modules, Global Positioning System(GPS) units, Inertial Measurement Unit (IMU) modules, stereoscopicvision or stereo vision modules, object recognition modules, deeplearning modules, machine learning modules, accelerometers, gyroscopes,compass units, odometry, computer vision modules, machine visionmodules, Artificial Intelligence (AI) modules, vehicular processor,vehicular computer, dashboard processor, dashboard computer,communication devices or the like. In some embodiments, an autonomousvehicle may utilize a Bayesian simultaneous localization and mapping(SLAM) module or algorithm to fuse and process data from multiplesensors and online/offline maps; and/or detection and tracking of othermoving objects (DATMO) modules; and/or real-time locating system (RTLS)beacon modules, high resolution real time maps (“HD maps”) and/or othersuitable modules.

In some embodiments, a vehicular processor or vehicular computer orother vehicular (in-vehicle) controller, may command one or more partsor components of the vehicle to perform certain operations, based on thesensed data and/or based on the processing of such data and/or based oninsights derived from such sensed data or from remote sensors or fromthe infrastructure or other off-vehicle sensors or data. For example, anautonomous car travels along an urban street at day-light; a highresolution map (“HD map”) is downloaded into the vehicular processor viaa communication module or device; the sensors of the autonomous carcollect data, and the vehicular processor determines from the captureddata that the road is clear and that there are no obstacles or any othervehicles or pedestrians, and that the car is currently traveling at aconstant speed of 10 kilometers per hour, and that the speed limit onthis street is 30 kilometers per hour; accordingly, the vehicularprocessor sends a command or a signal to the driving unit(s) of the car,to gradually increase the speed of the car from 10 to 25 kilometers perhour, over a period of 6 seconds. The driving unit(s) of the car performthis command, for example, by opening or releasing a valve that injectsor provides additional fuel or gas to the engine of the car, similar tothe manner in which a vehicular gas pedal operates in response to ahuman pressing of the gas pedal, controlling the direction of the wheelssimilarly to the human controlling the wheels of a car and so forth.

Subsequently, as the autonomous car travels further, newly captured dataor freshly acquired data enables the vehicular computer to identify atraffic light that is estimated to be located approximately 40 metersahead, which now turns from green light to red light; and in response tosuch detection, the vehicular computers sends to the vehicular drivingunit(s) commands instructing them (i) to stop the injection or theaddition of gas or fuel to the engine, immediately or abruptly orgradually over a period of 1.5 seconds; and (ii) to avoid adding of gasand to avoid braking (slowing down) for a period of 2 seconds; and then(iii) to slow-down or brake gradually to a full stop over a period of 7seconds. These commands may be updated on an ongoing basis, as thevehicle travels further and approaches the traffic light, and asfreshly-sensed data is acquired and provides the vehicular computer newinformation to maintain its previous commands or to modify them. Thevehicular driving unit(s) execute the commands that they received; forexample, by closing a valve that thus stops injection or addition of gasor fuel to the engine, and/or causing the power supply to be reduced orcut for an electrical engine or vehicle or a hybrid vehicle, and/or bytemporarily disconnecting between the engine and the tires or wheels ofthe vehicle by using a clutch (or similar, or equivalent) mechanism,and/or by activating the braking system of the vehicle similar to themanner in which a braking system operates when commanded to brake via amanual pressing of the brake pedal by a human driver.

Subsequently, the vehicular processor determines that in order to reacha particular destination that was pre-defined to the autonomous vehicle,in view of current road traffic conditions and in view of the currentlocation of the vehicle, there is a need to make a left turn at the nexttraffic light junction. Accordingly, the vehicular processor sendscommands to the vehicular steering unit(s), instructing them to steerthe vehicle gradually from the right lane of the road to the left laneof the road (while signaling to the left, and while constantly checkingthe surroundings via sensors); and further instructing them to steer thevehicle at the next traffic light to make a 90-degrees left turn at areduced speed of 18 kilometers per hour. The vehicular steering anddriving units execute such commands; for example, by activating andlater de-activating the left-turn signaling light of the vehicle, and bysteering the wheels or tires of the vehicle to the left side similar tothe operations that a vehicle performs in response to a manual turningof a steering wheel by a human operator.

Furthermore, the vehicle or the autonomous vehicle may be equipped withfurther units that enable remote driving or tele-operating of thevehicle. For example, the sensed data or a portion thereof, and/or thelocally-processed data or a portion thereof, or a diluted or compressedor encoded or sparse version of such data, is transmitted periodicallyand/or continuously and/or intermittently and/or when needed to a remoteserver, over one or more communication links (e.g., cellular, Wi-Fi,satellite, V2X, or the like); the remote server receives the data, mayfurther process the data as needed, may utilize an AI module or a remotehuman tele-operator to reach the relevant decisions similar to thedecision described above, for example, a remote decision to increase ordecrease the speed of the vehicle, a remote decision to increase ordecrease the amount of gas or fuel that is supplied or provided to theengine of the vehicle, a remote decision to activate the braking systemor the brakes of the car, a remote decision to temporarily disconnectbetween the engine and the wheels, a remote decision to steer ormaneuver the vehicle to the right or to the left or in other directions,a remote decision to perform an abrupt or immediate or emergency brakingor stopping, or other remote decisions performed at the tele-operatorsystem and/or human tele-operator. Such command(s) are then transmittedfrom the tele-operator entity to the vehicle, via IP connection or routeor path or multiplicity of communication operators and networks andhop(s), whereas the last hop from the network to/from the vehicle may beover one or more wireless communication links (e.g., cellular, Wi-Fi,satellite, V2X, V2V, V21, DSRC, or the like); and the vehicular computerreceives such commands, and sends the corresponding signals or commandsto the relevant vehicular units (e.g., braking unit, steering unit,driving unit, engine unit, electric unit, hydraulic unit, pneumaticunit, wheels, signaling, communications, gear, control, sensors, trailersystems, truck-related systems, bus-related systems, tractor-relatedsystems, infotainment, power, air-conditioning, emergency systems,safety systems, or any other system in the vehicle or connected to it orassociated with it or controlled from it that may be modified or managedor changed or powered either locally or remotely); and those vehicularunits perform the required operations based on the received commands orsignals.

It is noted that the above description is a demonstrative example ofsome embodiments of an autonomous vehicle or a tele-operated vehicle;and that the present invention may be utilized in conjunction with othertypes or mechanisms of automounts vehicles and/or tele-operatedvehicles.

The examples above are non-limiting examples that demonstratetele-operation or “autonomous vehicle” local or remote operation ortele-operation. Further, similar or additional examples may also pertainand apply to other autonomous vehicles, such as wheelchair, ship, boat,submarine, drone, UAV, or others, either as is or with the relevantadaptation of each system.

In some embodiments, the systems and methods of the present inventionmay reduce and/or improve the cognitive load or cognitive workload orcognitive task-load of a local human operator and/or of a remotetele-operator, which may sometimes be busy or occupied or distracted orbored or semi-conscious or even unconscious (e.g., a human driver thatfaints or becomes incapacitated during manual driving); and maycontribute to the safety and well-being of person(s) located within thevehicle, of person(s) located nearby (e.g., in other vehicles, orpedestrians), and/or may contribute to the safety of transported goodsor cargo.

In some embodiments, the system may comprise a communications-based mapgenerator 191, able to generate a communications-based map (e.g., byutilizing data received from one or more vehicles, in conjunction with amapping application), which indicates at least (i) a first road-segmenthaving effective wireless communication throughput that is below apre-defined threshold value, and (ii) a second road-segment havingeffective wireless communication throughput that is above saidpre-defined threshold value. The communications-based map generator 191is shown as being, for example, part of the remote server; however, itmay be implemented at or within the vehicle, or at or withininfrastructure elements, cellular nodes or elements, wirelesscommunication elements or nodes, or the like.

In some embodiments, the vehicle may comprise a navigation routegenerator 192, able to generate or modify a navigation route for saidvehicle (e.g., in in conjunction with a conventional route guidanceapplication or navigation application), by taking into account at least:an estimated level of wireless communication availability at differentroute segments; or, by taking into account at least: (i) an estimatedlevel of wireless communication availability at different routesegments, and also (ii) one or more constraints regarding safetyrequirements for passengers of said vehicle; or, by generating ormodifying a navigation route for said vehicle to include road-segmentshaving wireless communication availability that is above a pre-definedthreshold value; or, configured to increase safety of travel of saidvehicle which is a tele-operated vehicle, by generating or modifying anavigation route for said vehicle to include road-segments in whichtele-operation of said vehicle is estimated to be successful beyonda—pre-defined threshold level; or, configured to perform other routemodification or route determination operations in view of safetyconsiderations, number or age or gender or characteristics of driverand/or of passengers, the current and/or momentary and/or predictedand/or estimated level or quality of wireless communication(s) that isavailable for the vehicle to upload sensed data, the current and/ormomentary and/or predicted and/or estimated level or quality of videoand/or images and/or audio and/or other data that is sensed by vehicularsensor(s) and is uploaded to a remote AI unit for remote processing.

It is noted that in some embodiments, a “remote” AI unit or a “remote”processor, relative to a particular vehicle, need not necessarily be faraway in geographical distance relative to said vehicle; but rather, maybe any suitable AI unit or processor that is external to that vehicleand/or that is not physically connected to said vehicle and/or that isnot physically touching said vehicle; such as, for example, a “remote”AI unit or processor that is actually located in a smart road sign whichis currently located three meters away from the current location of thevehicle; or a “remote” AI unit or processor that is actually located atan eNodeB element which happens to be 24 meters away from the vehicle asit passes near it; or a “remote” AI unit or processor which is actuallylocated within a second vehicle that happens to be 6 meters away fromsaid particular vehicle; or other such “remote” AI units or processorsthat may be cloud-based and physically distant from the vehicle, or maybe actually nearby to that particular vehicle at that particular pointin time. In some embodiments, the “remote” AI unit or processor is fixedor non-mobile or non-moving (temporarily, or fixedly); whereas, in otherembodiments, the “remote” AI unit or processor may be moving (e.g.,within a second vehicle nearby; within a drone flying above or nearby;or the like).

Reference is made to FIG. 2, which is a schematic illustration of asystem 200, in accordance with some demonstrative embodiments of thepresent invention. System 200 may be a demonstrative implementation ofsystem 100 of FIG. 1; and the components of system 200 may include oneor more, or some, or all, of the components or modules shown in FIG. 1.

System 200 may include one or more vehicles, for example, a vehicle 210(which may be referred to as a “primary” vehicle) and other vehicles 211and 212 (which may be referred to as “secondary” vehicles). The vehicles210-212 may be travelling along the same route or on different routes,or on the same road or road-segment or in different roads, or in thesame direction or in opposite directions or in perpendicular directionsor in other non-identical directions. Other devices, entities and/orusers may also appear or may be nearby or may be part of system 200;such as pedestrians, cyclists, motorcyclists, scooters, animals, movingor non-moving objects such as balls, potholes, or others; road hazards,flying drones, self-driving vehicles, autonomous vehicles, tele-operatedvehicles, electric vehicles, or the like; and some of these may also bereferred to as “secondary vehicles”, such as the pedestrians ormotorcycles or cyclists, if they are operably connected to the primaryvehicle, in an ad-hoc way or in other way, or if they are equipped withthe ability to send and/or receive wireless communication signals to theprimary vehicle and/or to a remote server and/or to other entities orunits of system 200.

Each one of the units in system 200, may comprise one or moretransceivers able to send and receive data and/or packets; as well as anAI unit or AI processor or AI module, able to process (partially orfully) some or all of the data that was sensed or collected by theprimary vehicle 210.

An external or remote server/AI unit 270 may be in communication(directly, or indirectly) with one or more of the vehicles 210-212, andparticularly with the primary vehicle 210. The external or remoteserver/AI unit 270 is external to each one of the vehicles 210-212, andis separated from each one of the vehicles 210-212; and may be remote indistance (e.g., may be located 100 or 5,000 meters away from thevehicles 210-212), or may be extremely remote in distance (e.g., may beimplemented as a remote cloud-computing server that is located inanother city or another state or another country and is accessible viaIP communication network including cellular communication links); or maybe physically nearby relative to the vehicles 210-212 or to some ofthem. Each vehicle 210-212 may upload (or stream in real time or in nearreal time, or via delayed streaming or subsequent streaming, or via fileupload or via data upload) or otherwise transmit to the external orremote server/AI unit 270, data collected by or sensed by one or moresensors of such vehicle; and such data may be processed by the externalor remote server/AI unit 270, which in turn may provide tele-operatingcommands to one or more of the vehicles 210-212.

A Tele-Operator Terminal/Human-Machine Interface (HMI) 255 for the humantele-operator may also be in communication (directly, or indirectly)with one or more of the vehicles, such as with the primary vehicle 210;and may enable a human tele-operator or a machine-based tele-operator toremotely interact, intervene, operate or drive or control the primaryvehicle 210 or otherwise affect its operation via remote commands orremote suggestions or remotely-obtained data or remotely-generatedcommands. The HMI units of the Tele-Operator Terminal/HMI 255 mayinclude, for example, touch screen, screen, joystick, touch-pad,computer mouse, steering wheel, pedals, gear shift device, microphone,speakers, Augmented Reality (AR) or Virtual Reality (VR) equipment(e.g., wearable device, helmet, headgear, glasses, googles, gloves, orthe like), haptic elements, tactile elements, gloves, other wearableelements, lights, alarms, or the like.

Optionally, the Tele-Operator Terminal/HMI 255 may be in communicationwith (or may be associated with) the external or remote server/AI unit270; for example, if one or more conditions hold true, the external orremote server/AI unit 270 may transfer the tele-operation control ofvehicle 210 to the human tele-operator, or may take away thetele-operation control of vehicle 210 from the human tele-operator, ormay provide suggested or recommended tele-operation commands for thehuman tele-operator to further approve or reject, or the like.

A cellular network element 283, such as eNodeB or gNodeB or another 3Gor 4G or LTE or 4G-LTE or 5G network element, or a cellular tower orcellular base station or a fixed cellular transceiver, may further be incommunication with the vehicle 210 (and optionally with other vehicles,and/or with other units of system 200); may receive from the vehicle 210sensed data collected by the vehicular sensors of vehicle 210; mayoptionally perform AI processing of such data, locally within thecellular network element 283 and/or remotely by sending data and/orqueries to the external or remote server/AI unit 270; and may thentransmit tele-operating commands or tele-operating suggestions or othertele-operated data to vehicle 210.

Similarly, an Access Point (AP) 281, which may be aWi-Fi/V2X/Satellite-based/Other (e.g., fixed or ad hoc) access point orcommunications node, which may comprise one or more transceivers of thesame types or of different types, may further be in communication withthe vehicle 210 (and optionally with other vehicles, and/or with otherunits of system 200); may receive from the vehicle 210 sensed datacollected by the vehicular sensors of vehicle 210; may optionallyperform AI processing of such data, locally within the AP 281 and/orremotely by sending data and/or queries to the external or remoteserver/AI unit 270; and may then transmit tele-operating commands ortele-operating suggestions or other tele-operated data to vehicle 210.

Similarly, one or more road-side or in-road or over-the-roadinfrastructure element(s) 260, may further be in communication with thevehicle 210; such infrastructure element(s) 260 may be, for example, atraffic light, a traffic sign, a road sign, a bridge, a tunnel, anInternet of Things (IoT) device or an IP-connected sensor, or the like;they may receive from the vehicle 210 sensed data collected by thevehicular sensors of vehicle 210; may perform AI processing of suchdata, locally within the infrastructure element(s) 260 and/or remotelyby sending data and/or queries to the external or remote server/AI unit270; and may then transmit tele-operating commands to vehicle 210.

Optionally, other vehicles 211-212 may be in communication with theprimary vehicle 210, directly and/or indirectly, such as via one or moread-hoc networks, DSRC, V2V, peer-to-peer communication, or otherprotocols and topologies; may receive from vehicle 210 sensed data; mayperform some or all of the AI processing of such data, locally withinsuch vehicles 211-212 and/or remotely by sending data and/or queries tothe external or remote server/AI unit 270; and may then transmittele-operating commands to vehicle 210.

In some embodiments, optionally, vehicles 211-212 may further assist theprimary vehicle 210 in uploading and/or streaming data that was sensedor collected by vehicular sensors of vehicle 210, to the external orremote server/AI unit 270 and/or to other units of system 200. Forexample, vehicle 210 may capture video via a vehicular video camera, andmay transmit the captured video and/or the real-time streaming video tonearby vehicle 211 over a direct ad hoc Wi-Fi link between these twovehicles; and the vehicle 211 may relay or send or transmit or upload orstream such video to the external or remote server/AI unit 270 oranother processor such as may be used for or in or by the Tele-OperatorTerminal/HMI 255, over a 4G-LTE communication link or over a 5Gcommunication link, together with an indication that this data wasactually sensed by (and belongs to) the primary vehicle 210 and not tothe relaying vehicle 211, and that tele-operating commands that arebased on this data should be transmitted to the primary vehicle 210(e.g., directly, or indirectly via the relaying vehicle 211 while suchcommands include an indication that they are directed to the primaryvehicle 210).

In some embodiments, optionally, the secondary vehicle 211 itself, mayalso utilize some or all of the data that it is relaying from vehicle210 to another unit; for example, if such data includes informationabout a road hazard or an environmental condition (e.g., fog, snow, orthe like) that affects the primary vehicle 210 and is also estimated toaffect the secondary vehicle(s) such as vehicle 211.

In some embodiments, the data (e.g., sensed data that is uploaded fromthe vehicle, and/or processed data or commands that is downloaded orsent towards the vehicle) may be transmitted via broadcast or unicast ormulticast, or via a single one-to-one communication link, or via asingle point-to-point communication link, or via a one-to-manycommunication link or channel, or via a set or group or batch of severalone-to-one communication links or of several point-to-pointcommunication links; such as between (or among) the primary vehicle andany subset of the secondary vehicles and/or infrastructure elementsand/or the external or remote server or AI unit, in either direction(upstream and/or downstream; uplink and/or downlink; upload and/ordownload); using half-duplex or using full-duplex communication;optionally with an additional control channel or messaging channel orfeedback channel, or without such additional channel. Some embodimentsmay utilize IP connectivity without necessarily knowing the address of aspecific destination, or may allow sending of data from one sender tomultiple receivers or a group of receivers which may be listening-in onthe same IP network and without necessarily knowing in advance or at thetime of the transmission whether and/or how many recipient units areactually listening-in to such transmission or broadcast or multicast orupload or uplink Some of the transmitted information may be relevant tomore than one destination device (or recipient, or vehicle, or roaduser), and therefore some embodiments may utilize broadcasting and/ormulticasting as being more efficient and/or having lower delay indelivering the data to more than a threshold number of IP destinationsor recipients or information consumers or data processors.

Reference is made to FIG. 3, which is a flow-chart of a method inaccordance with some demonstrative embodiments of the present invention.The method may be implemented by one or more, or some, or all, of theunits of system 100 or system 200, or by other suitable systems ordevices.

In some embodiments, a primary vehicle senses and collects data viavehicular sensors (block 310). The sensed data is uploaded ortransmitted to an external AI unit (block 320), via one or more wirelesscommunication links, and particularly via a bonded (or aggregated, ormulti-link) virtual communication link comprised of multiple wireless IPcommunication links served by multiple vehicular transceivers, such thateach transceiver is able to transmit or upload or stream, in parallel,some of the total packets that need to be uploaded to a certain IPdestination address, and such that the recipient unit utilizes anassembly engine or re-assembly engine which re-assembles the packetsthat are incoming from multiple wireless links into an ordered andcorrect data-stream.

The external AI unit performs AI processing of the vehicular-sensed data(block 330), optionally in combination with other data that is availableto the AI unit from other sources (e.g., weather data, seismic data,traffic data, or the like).

The external AI unit (and/or the human tele-operator using thetele-operator terminal/HMI 255) generates one or more tele-operatingcommands, and transmits them back to the vehicle (block 340); optionallywith a dis-engagement command, or with a signal that the vehicle is nowswitched to being tele-operated by the external AI unit. The signalssent to the vehicle may represent or may carry, for example: (a)tele-operating commands, (b) and/or direct tele-operating commands thatcan be immediately executed by the specific recipient vehicle withoutfurther conversion or re-formatting or translation, and/or (c) indirecttele-operating commands that the specific recipient vehicle thentranslates or converts or re-formats into specific executable in-vehiclecommands, and/or (d) input to the vehicle that is expressed asconditional statements and/or conditional commands and/or by usingBoolean operators or Boolean logic (e.g., “if you can come to a completestop within 3 seconds then do so, and if not then change the lane to theright lane and reduce your speed from 40 to 20 mph”), and/or (e) inputto the vehicle that comprises data sense and/or processed and/orcollected and/or obtained by the remote AI unit or the remotetele-operator, such that the vehicle's systems may utilize suchadditional data in order to reach an in-vehicle decision (e.g., weatherdata, vehicular traffic data, seismic data, data from law enforcementsystems, data that was recently or previously sensed or uploaded orup-streamed by other vehicle(s) and/or by infrastructure elements and/orby IP-connected sensors or devices), and/or (f) control data ormessaging data or feedback data or other meta-data (e.g.,time-stamp/date-stamp, a unique identifier of the vehicle that thistransmission is intended to, a unique identifier of the remote AI unitor the remote tele-operator that provided or transmitted the data, anindication whether the sent data is a strict command that must befollowed or whether it is only an option or a suggestion or arecommendation that the recipient vehicle may or may not accept orreject or modify, or the like), and/or other types of data or signals.

Additionally or alternatively, the external AI unit or the humantele-operator using the tele-operator terminal/HMI 255 may refer orrelay or switch or re-direct or allocate or submit the tele-operation ofthe vehicle to another human tele-operator and/or to anothermachine-based tele-operator (block 350), which in turn proceeds toprovide tele-operating commands to the vehicle, or which in turnproceeds to further relay or re-direct to another entity.

Reference is made to FIG. 4, which is a block-diagram illustration of asub-system 400, in accordance with some demonstrative embodiments of thepresent invention. Sub-system 400 may be part of a vehicle, or may bewithin a vehicle, or may be a vehicular add-on unit or may be mounted onor within a vehicle, or may be embedded or integrated in a vehicle orwithin a vehicle or under or beneath a vehicle, and/or may beimplemented as a vehicular component or as part of a vehicular dashboardor vehicular communication unit; or may be otherwise associated with avehicle, or may be used by a user or driver or passenger of a vehicle.Sub-system 400 may be part of any vehicle of System 100 or of System 200described above, or of other devices or apparatuses in accordance withthe present invention.

Sub-system 400 may comprise multiple input connectors 401 or sockets orports or outlets or inlets or male-parts or female-parts, or other inputinterface, enabling the sub-system 400 to connect mechanically and/orphysically and/or electronically and/or via a wire or cable and/or viaother types of connection, to one or more vehicular sensors 451 (e.g.,cameras, imagers, video cameras, microphones, LIDAR sensors, RADARsensors, proximity sensors, temperature sensors, humidity sensors, orthe like); and enabling the sub-system 400 to receive data from suchvehicular sensors, as well as from other vehicular units 452 (e.g.,vehicular computer, vehicular processor, self-driving processor,autonomous driving processor; electric control unit (ECU) 454 orelectric control module, or electronic control unit or module; vehicularmapping system, vehicular navigation sub-system 455, vehicularroute-guidance system; Global Positioning Unit (GPS) element or system;or the like).

Optionally, a memory unit 402 (e.g., Flash memory, Random Access Memory(RAM), or the like) and/or a storage unit 403 (e.g., Hash memory, solidstate drive (SSD), hard disk drive (HDD), or the like) may store suchdata that was sensed by (or collected from) the vehicular sensors.

Optionally, a processor 404 may partially or fully process such data, ormay re-format or convert it, or may encode or re-encode or trans-codeit; particularly for the purposes of efficiently uploading such data toan external or remote recipient over one or more wireless communicationlinks.

Sub-system 400 may optionally comprise also an AI processor 415, whichmay perform some or all of the AI processing of the sensed data and/orother known conditions (e.g., vehicle's velocity, location, or the like)in order to generate commands and/or signals that are then performed orutilized by a self-driving or autonomous driving unit of the vehicle. Inother embodiments, such AI processor need not necessarily be comprisedin sub-system 400, but rather, may be connected to it, or may be part ofa vehicular AI unit 452 which is coupled to (or operably associatedwith) the sub-system 400.

A packet allocator 405 may allocate packets that are intended fortransmission to the remote or external entity, to one or moretransceivers; such that each transceiver sends out or transmits adifferent batch or set or group of the total packets that are intendedto be uploaded or sent out. Optionally, a link bonding unit 406 maydefine, determine, configure and/or modify, in a dynamic manner, whichcommunication link(s) to utilize and/or which transceivers to utilizefor such uplink transmission, thereby generating an ad hoc or acontinuous bonded channel or bonded multi-link connection.

The packet allocator 405 and/or the link bonding unit 406 may operate incooperation with such multiple transceivers, which may be transceivers408 that are integral or internal or integrated or embedded parts of thesub-system 400, and/or which may be transceivers 453 which are externalto the sub-system 400 yet accessible to it via output connectors 407 orsockets or cables or wires (or other output interface) which maytransfer such data or packet to the suitable transceivers fortransmitting.

In some embodiments, optionally, sub-system 400 may be connected to orassociated with or coupled to, or may be in communication with, aself-driving/autonomous driving/tele-operation unit 456 of the vehicle;such as, in order to receive data from such unit, and/or in order totransfer to such unit locally-generated commands and/orremotely-generated commands for operating or tele-operating of thevehicle.

Some embodiments of the present invention may comprise, or may utilize,one or more components, units, devices, systems, and/or methods that aredisclosed in U.S. Pat. No. 7,948,933, titled “Remote transmissionsystem”, which is hereby incorporated by reference in its entirety.

Some embodiments of the present invention may comprise, or may utilize,one or more components, units, devices, systems, and/or methods that aredisclosed in U.S. Pat. No. 9,826,565, titled “Broadband transmitter,broadband receiver, and methods thereof”, which is hereby incorporatedby reference in its entirety.

Some embodiments of the present invention may comprise, or may utilize,one or more components, units, devices, systems, and/or methods that aredisclosed in PCT international application number PCT/IL2018/050484,titled “Device, system, and method of pre-processing and data deliveryfor multi-link communications and for media content”, published asinternational publication number WO 2018/203336 A1, which is herebyincorporated by reference in its entirety.

Some embodiments of the present invention may comprise, or may utilize,one or more components, units, devices, systems, and/or methods that aredisclosed in PCT international application number PCT/IL2017/051331,titled “Device, system, and method of wireless multiple-link vehicularcommunication”, published as international publication number WO2018/211488 A1, which is hereby incorporated by reference in itsentirety.

In some embodiments, a system comprises: a vehicular processor that isassociated with a vehicular Artificial Intelligence (AI) unit, that isconfigured: (a) to receive inputs from a plurality of vehicular sensorsof a vehicle, (b) to locally process within said vehicle at least afirst portion of said inputs, (c) to wirelessly transmit via a vehicularwireless transmitter at least a second portion of said inputs to aremote tele-driving processor located externally to said vehicle, (d) towirelessly receive, via a vehicular wireless receiver from said remotetele-driving processor, a remotely-computed processing result that isreceived from a remote node, and (e) to implement a vehicular operatingcommand based on said remotely-computed processing result, via at leastone of: (i) an autonomous driving unit of said vehicle, (ii) atele-driving unit of said vehicle, (iii) a vehicular driving processor,(iv) a vehicular commands translator unit.

In some embodiments, the vehicular processor that is associated withsaid vehicular AI unit is to transfer said vehicular operating commandto an autonomous driving unit of said vehicle which then autonomouslyoperates said vehicle based on said vehicular operating command. In someembodiments, the vehicular processor that is associated with saidvehicular AI unit is to transfer said vehicular operating command to atele-driving unit of said vehicle which then autonomously operates saidvehicle based on said vehicular operating command.

In some embodiments, the system comprises: a vehicular autonomousdriving unit, configured to autonomously operate said vehicle based onboth (I) inputs generated locally within said vehicle by said AI unit,and (II) said vehicular operating command received from said remotetele-driving processor.

In some embodiments, the system comprises: a dynamic encoder todynamically encode said second portion of said inputs into areduced-size representation prior to transmission to said remotetele-driving processor. In some embodiments, said data-sharingdetermination unit operates by taking into account at least a level ofcertainty of said vehicular AI unit in a locally-computed processingresult that was computed locally in said vehicle by said vehicular AIunit.

In some embodiments, the system comprises: a communication channelsallocator, to allocate a first set of packets having data from a firstvehicular sensor, for wireless transmission via a first set of one ormore vehicular wireless transmitters, and to allocate a second set ofpackets having data from a second vehicular sensor, for wirelesstransmission via a second set of one or more vehicular wirelesstransmitters.

In some embodiments, the communication channels allocator is todynamically determine which one or more vehicular transmitters are to beincluded in the first set of transmitters, and which one or morevehicular transmitters are to be included in the second set oftransmitters.

In some embodiments, the communication channels allocator is todynamically determine, which one or more vehicular transmitters are tobe included in the first set of transmitters, and which one or morevehicular transmitters are to be included in the second set oftransmitters; wherein the first set of transmitters and the second setof transmitters include at least one particular transmitter that iscommon to both of said sets.

In some embodiments, the system comprises: a communication channelsallocator, to allocate a first wireless communication channel forwireless transmission of data collected by a first vehicular sensor to afirst remote processor for processing at said first remote processor,and to allocate a second wireless communication channel for wirelesstransmission of data from a second vehicular sensor to a second remoteprocessor for processing at said second remote processor.

In some embodiments, the system comprises: a vehicular multipletele-operators handling unit, (A) to wirelessly receive a firsttele-driving command from a first tele-driving processor, (B) towirelessly receive a second tele-driving command from a secondtele-driving processor, (C) to detect an inconsistency between the firstand second tele-driving commands, wherein said inconsistency comprisesat least one of: contradiction, mismatch, duplication, adverse effects,contradictory results; and (D) to determine, based on pre-defined rulesor by using the vehicular AI unit, which tele-operating command todiscard and which tele-operating command to execute in said vehicle.

In some embodiments, said remote tele-driving processor is a movingtele-driving processor that is located in a secondary vehicle; whereinthe vehicular AI unit is to wirelessly transmit said second portion ofinputs to the tele-driving processor located in said secondary vehicle;wherein the vehicular AI unit is to wirelessly receive from saidsecondary vehicle the remotely-computed processing result; wherein thevehicular AI unit is to generate said vehicular operating command basedon said remotely-computed processing result that was wirelessly receivedfrom said secondary vehicle.

In some embodiments, said remote tele-driving processor is atele-driving processor that is external to said vehicle and is locatedin a non-mobile traffic infrastructure element; wherein the vehicular AIunit is to wirelessly transmit said second portion of inputs to thetele-driving processor located in said non-mobile traffic infrastructureelement; wherein the vehicular AI unit is to wirelessly receive fromsaid non-mobile traffic infrastructure element the externally-computedprocessing result; wherein the vehicular AI unit is to generate saidvehicular operating command based on said remotely-computed processingresult that was wirelessly received from said non-mobile trafficinfrastructure element.

In some embodiments, the system comprises: a communications-based mapgenerator, to generate a communications-based map which indicates atleast (i) a first road-segment having effective wireless communicationthroughput that is below a pre-defined threshold value, and (ii) asecond road-segment having effective wireless communication throughputthat is above said pre-defined threshold value.

In some embodiments, the system comprises: a navigation route generatorto generate or to modify a navigation route for said vehicle, by takinginto account at least: an estimated level of wireless communicationservice availability at different route segments.

In some embodiments, the system comprises: a navigation route generatorto generate or to modify a navigation route for said vehicle, by takinginto account at least: (i) an estimated level of wireless communicationavailability at different route segments, and also (ii) one or moreconstraints regarding safety requirements for passengers of saidvehicle.

In some embodiments, the system comprises: a navigation route generator,to increase safety of travel of said vehicle which is a self-drivingvehicle, by generating or modifying a navigation route for said vehicleto include road-segments having wireless communication availability thatis above a pre-defined threshold value.

In some embodiments, the system comprises: a navigation route generator,to increase safety of travel of said vehicle which supports beingtele-operated, by generating or modifying a navigation route for saidvehicle to include road-segments in which tele-operation of said vehicleis estimated to be successful.

In some embodiments, a self-driving unit of said vehicle is todynamically modify one or more driving parameters of said vehicle, basedon availability of wireless communications at one or more particularroute segments.

In some embodiments, a self-driving unit of said vehicle is todynamically modify one or more driving parameters of said vehicle, basedon estimated success level of remote tele-operation of said vehiclebased on tele-operating commands received from a remote machine-based orhuman tele-operator or from a remote AI processing unit.

In some embodiments, a self-driving unit of said vehicle is todynamically reduce a driving speed of said vehicle or to dynamicallyincrease a distance from a nearby vehicle, based on reduced availabilityof wireless communications at a particular route segment.

In some embodiments, a self-driving unit of said vehicle is todynamically reduce a driving speed of said vehicle or to dynamicallyincrease a distance from a nearby vehicle, based on a reduced estimatedlevel of success of remote tele-operation of said vehicle at aparticular route segment.

In some embodiments, a self-driving unit of said vehicle is todynamically reduce a driving speed of said vehicle or to dynamicallyincrease a distance from a nearby vehicle, at a particular routesegment, based on reduced throughput of wireless video upload from saidvehicle to a remote recipient that includes a remote tele-operationterminal or a remote AI processing unit.

In some embodiments, an external AI module, that is located externallyto said vehicle, is to take over the driving of said vehicle instead ofsaid vehicular AI unit or instead of an in-vehicle human driver.

In some embodiments, an external AI module, that is located externallyto said vehicle, is to take over the driving of said vehicle instead ofsaid vehicular AI unit or instead of an in-vehicle human driver; whereinsaid external AI module is to selectively refer the tele-operation ofsaid vehicle to a remote human tele-operator.

In some embodiments, an external AI module, that is located externallyto said vehicle, is to take over the driving of said vehicle instead ofsaid vehicular AI unit or instead of an in-vehicle human driver; whereinsaid external AI module is to adjust a level of confidence in remotetele-operating operations, based on (i) quality of wirelesscommunications received from said vehicle, and (ii) quality of senseddata that is sensed by vehicular sensors of said vehicle and is uploadedto said external AI module.

In some embodiments, data sensed by a single vehicular sensor of saidvehicle, is transmitted via a bonded communication uplink to an externalAI unit that is located externally to said vehicle; wherein packets thatcorrespond to data sensed by said single sensor of said vehicle, areuploaded from said vehicle by: (i) allocating a first set of packets tobe uploaded via a first wireless communication link by a first wirelesstransmitter associated with said vehicle, and (ii) allocating a secondset of packets to be uploaded via a second wireless communication linkby a second wireless transmitter associated with said vehicle.

In some embodiments, said bonded communication uplink is utilized bysaid vehicle to increase a confidence level of one or more remote AImodules that generate tele-operation commands for said vehicle based ondata sensed by said vehicle and uploaded to said one or more remote AImodules.

In some embodiments, said bonded communication uplink is dynamicallyconstructed by said a link bonding unit associated with said vehicle, byselecting two or more of any combination of: a cellular communicationlink, a Wi-Fi communication link, a V2X communication link, asatellite-based communication link, a Direct Short-Range Communication(DSRC) communication link.

In some embodiments, the vehicular transmitter or a transmitterassociated with the vehicle, is to upload, to an external AI module thatis external to said vehicle, data sensed by one or more vehicularsensors of said vehicle; wherein the vehicular AI unit is to receivefrom said external AI module, a batch of two or more conditionaltele-operating commands for said vehicle, and to perform a firsttele-operating command if a first condition holds true at a particularlevel of confidence, and to perform a second tele-operating command if asecond condition holds true at a particular level of confidence.

In some embodiments, a wireless transmitter associated with said vehicleis to upload, to an external AI module that is external to said vehicle,data sensed by one or more vehicular sensors of said vehicle; whereinthe vehicular AI unit is to receive from said external AI module, anauthorization to perform in-vehicle processing of said sensed data andto avoid waiting for an incoming tele-operation command.

In some embodiments, a transmitter associated with said vehicle is toupload, to an external AI module that is external to said vehicle, datasensed by one or more vehicular sensors of said vehicle, wherein saiddata is at least partially processed within said vehicle to enableeffective upload of said data to said external AI module based oneffective wireless communication resources that are currently availableto said vehicle using a multiplicity of wireless transceivers.

In some embodiments, a transmitter associated with said vehicle is toupload, to an external AI module that is external to said vehicle, datasensed by one or more vehicular sensors of said vehicle; wherein if aquality indicator of effective wireless communication resources that arecurrently available to said vehicle for said upload, is greater than apre-defined threshold value, then the external AI module is to determinea first level of certainty for a tele-operating command generated by theexternal AI module; wherein if the quality indicator of effectivewireless communication resources that are currently available to saidvehicle for said upload, is smaller than said pre-defined thresholdvalue, then the external AI module is to determine a second, reduced,level of certainty for said tele-operating command generated by theexternal AI module.

In some embodiments, a transmitter associated with said vehicle is toupload, to an external AI module that is external to said vehicle, datasensed by one or more vehicular sensors of said vehicle; wherein saidtransmitter comprises two or more transmitting units; wherein saidupload is performed over a bonded wireless communication link whichcomprises two or more wireless communications links that are servedconcurrently by two or more, respective, wireless transmitting unitsthat operate and upload different packets in parallel to each other.

In some embodiments, two or more transmitters that are associated withsaid vehicle are to upload, to an external AI module that is external tosaid vehicle, data sensed by one or more vehicular sensors of saidvehicle; wherein a first transmitter is to wirelessly transmit, to saidexternal AI module, data sensed by a first vehicular sensor of saidvehicle; wherein a second transmitter is to concurrently wirelesslytransmit, to said external AI module, data sensed by a second vehicularsensor of said vehicle.

In some embodiments, two or more transmitter that are associated withsaid vehicle are to upload, to an external AI module that is external tosaid vehicle, data sensed by a single vehicular sensor of said vehicle;wherein a first transmitter is to wirelessly transmit, to said externalAI module, a first batch of packets of data sensed by said singlevehicular sensor of said vehicle; wherein a second transmitter is toconcurrently wirelessly transmit, to said external AI module, a secondbatch of packets of data sensed by said single vehicular sensor of saidvehicle.

In some embodiments, a transmitter associated with said vehicle is toupload, to an external AI module that is external to said vehicle, datasensed by one or more vehicular sensors of said vehicle; wherein saidupload is performed over a bonded wireless communication link whichcomprises an aggregation of two or more wireless communications linksthat are served by two or more, respectively, wireless transmittersassociated with said vehicle.

In some embodiments, said bonded wireless communication uplink isdynamically constructed by a link bonding unit associated with saidvehicle, by selecting two or more communication links in any combinationout of: a cellular communication link, a Wi-Fi communication link, asatellite-based communication link, a V2X communication link, a DirectShort-Range Communication (DSRC) communication link; wherein the two ormore communication links operate concurrently and upload differentpackets in parallel to each other.

In some embodiments, the vehicular AI unit of said vehicle is part of amultiple-peers network of AI units, which are distributed among multiplevehicles and at least one AI unit that is external to all vehicles;wherein said multiple-peers network of AI units provides AItele-operating commands to at least one vehicle of said multiple-peersnetwork of AI units.

In some embodiments, the vehicular AI unit of said vehicle operates (I)to perform AI processing of data sensed by sensors of another vehicle,and (II) to reach an AI-based tele-operating command that is suitablefor said other vehicle, (III) to transmit said tele-operating command tosaid other vehicle.

In some embodiments, commands that are sent from a remote tele-operationterminal to said vehicular processor of said vehicles, are transmittedover a multiplicity of wireless communication transceivers that areassociated with said vehicular processor.

In some embodiments, said vehicular processor (i) operates to identifytwo or more repeated tele-operating commands incoming via the sametransceivers or via different transceivers, and (ii) executes a firstcommand of said repeated tele-operating command, and (iii) discards oneor more other commands of the repeated tele-operating commands.

In some embodiments, two or more wireless transceivers, that areassociated with said vehicle, and which have different momentaryperformance characteristics, are concurrently utilized to upload datafrom said vehicle to a remote tele-operation terminal or to downloaddata from said remote tele-operation terminal to said vehicle.

In some embodiments, the two or more wireless transceivers, that areassociated with said vehicle, have different momentary performancecharacteristics of at least one of: throughput, good-put, effectiveupload bandwidth, effective download bandwidth, latency, delays, errorrate, erroneous packet rate, missing packets rate.

In some embodiments, a link bonding unit that is associated with saidvehicle, operates to increase safety or accuracy or certainty level ofremotely-generated tele-operation commands that are directed to saidvehicle, by bonding together and allocating packets for upload across amultiplicity of wireless communication transceivers that are availableto said vehicle.

In some embodiments, at least a portion of packets, that represent datasensed by one or more vehicular sensors, is uploaded from said vehicleto a remote tele-operation terminal or to a remote AI module, via atransceiver of a smartphone or a tablet of a non-driver passenger ofsaid vehicle. In some embodiments, said system comprises said vehicle.

In some embodiments, an apparatus includes a cellular infrastructureelement which comprises: (a) a cellular receiver to wirelessly receivefrom a vehicular transmitter of a vehicle packets corresponding toinputs of one or more vehicular sensors; (b) a data processor integralto said cellular infrastructure element, to process said packets and togenerate a vehicular operational command; (c) a wireless transmitter towirelessly transmit to a wireless receiver of said vehicle a signalindicating said vehicular operational command generated by said cellularinfrastructure element. In some embodiments, the cellular infrastructureelement comprises at least one of: a cellular base station, a cellulartransmission tower, an eNodeB element, a gNodeB element, a fixednon-moving cellular transceiver node, a cellular edge infrastructureelement, an edge computing element.

In some embodiments, a device includes a Wi-Fi access point whichcomprises: (a) a Wi-Fi receiver to wirelessly receive from a vehiculartransmitter of a vehicle packets corresponding to inputs of one or morevehicular sensors; (b) a data processor integral to said Wi-Fi accesspoint, to process said packets and to generate a vehicular operationalcommand; (c) a wireless transmitter to wirelessly transmit to a wirelessreceiver of said vehicle a signal indicating said a vehicularoperational command generated by said Wi-Fi access point. In someembodiments, said Wi-Fi access point is to locally process a firstportion of said packets, and to forward a second portion of said packetsfor remote processing by a remote tele-driving processor or a remotetele-operation terminal.

In some embodiments, a device includes a satellite-based access pointwhich comprises: (a) a wireless receiver to wirelessly receive from avehicular transmitter of a vehicle packets corresponding to inputs ofone or more vehicular sensors; (b) a data processor integral to saidsatellite-based access point, to process said packets and to generate avehicular operational command; (c) a satellite-based wirelesstransmitter to wirelessly transmit to a wireless receiver of saidvehicle a signal indicating said a vehicular operational commandgenerated by said satellite-based access point. In some embodiments,said satellite-based access point is to locally process a first portionof said packets, and to forward a second portion of said packets forremote processing by a remote tele-driving processor or a remotetele-operation terminal.

In some embodiments, a device includes a DSRC or V2X access point whichcomprises: (a) a wireless receiver to wirelessly receive from avehicular transmitter of a vehicle packets corresponding to inputs ofone or more vehicular sensors; (b) a data processor integral to saidaccess point, to process said packets and to generate a vehicularoperational command; (c) a wireless transmitter to wirelessly transmitto a wireless receiver of said vehicle a signal indicating said avehicular operational command generated by said satellite-based accesspoint. In some embodiments, said DSRC or V2X access point is to locallyprocess a first portion of said packets, and to forward a second portionof said packets for remote processing by a remote tele-driving processoror a remote tele-operation terminal.

In accordance with embodiments of the present invention, calculations,operations and/or determinations may be performed locally within asingle device, or may be performed by or across multiple devices, or maybe performed partially locally and partially remotely (e.g., at a remoteserver) by optionally utilizing a communication channel to exchange rawdata and/or processed data and/or processing results.

Although portions of the discussion herein relate, for demonstrativepurposes, to wired links and/or wired communications, some embodimentsare not limited in this regard, but rather, may utilize wiredcommunication and/or wireless communication; may include one or morewired and/or wireless links; may utilize one or more components of wiredcommunication and/or wireless communication; and/or may utilize one ormore methods or protocols or standards of wireless communication.

Some embodiments may be implemented by using a special-purpose machineor a specific-purpose device that is not a generic computer, or by usinga non-generic computer or a non-general computer or machine. Such systemor device may utilize or may comprise one or more components or units ormodules that are not part of a “generic computer” and that are not partof a “general purpose computer”, for example, cellular transceivers,cellular transmitter, cellular receiver, GPS unit, location-determiningunit, accelerometer(s), gyroscope(s), device-orientation detectors orsensors, device-positioning detectors or sensors, or the like.

Some embodiments may be implemented as, or by utilizing, an automatedmethod or automated process, or a machine-implemented method or process,or as a semi-automated or partially-automated method or process, or as aset of steps or operations which may be executed or performed by acomputer or machine or system or other device.

Some embodiments may be implemented by using code or program code ormachine-readable instructions or machine-readable code, which may bestored on a non-transitory storage medium or non-transitory storagearticle (e.g., a CD-ROM, a DVD-ROM, a physical memory unit, a physicalstorage unit), such that the program or code or instructions, whenexecuted by a processor or a machine or a computer, cause such processoror machine or computer to perform a method or process as describedherein. Such code or instructions may be or may comprise, for example,one or more of: software, a software module, an application, a program,a subroutine, instructions, an instruction set, computing code, words,values, symbols, strings, variables, source code, compiled code,interpreted code, executable code, static code, dynamic code; including(but not limited to) code or instructions in high-level programminglanguage, low-level programming language, object-oriented programminglanguage, visual programming language, compiled programming language,interpreted programming language, C, C++, C#, Java, JavaScript, SQL,Ruby on Rails, Go, Cobol, Fortran, ActionScript, AJAX, XML, JSON, Lisp,Eiffel, Verilog, Hardware Description Language (HDL, BASIC, VisualBASIC, Matlab, Pascal, HTML, HTML5, CSS, Perl, Python, PHP, machinelanguage, machine code, assembly language, or the like.

Discussions herein utilizing terms such as, for example, “processing”,“computing”, “calculating”, “determining”, “establishing”, “analyzing”,“checking”, “detecting”, “measuring”, or the like, may refer tooperation(s) and/or process(es) of a processor, a computer, a computingplatform, a computing system, or other electronic device or computingdevice, that may automatically and/or autonomously manipulate and/ortransform data represented as physical (e.g., electronic) quantitieswithin registers and/or accumulators and/or memory units and/or storageunits into other data or that may perform other suitable operations.

The terms “plurality” and “a plurality”, as used herein, include, forexample, “multiple” or “two or more”. For example, “a plurality ofitems” includes two or more items.

References to “one embodiment”, “an embodiment”, “demonstrativeembodiment”, “various embodiments”, “some embodiments”, and/or similarterms, may indicate that the embodiment(s) so described may optionallyinclude a particular feature, structure, or characteristic, but notevery embodiment necessarily includes the particular feature, structure,or characteristic. Furthermore, repeated use of the phrase “in oneembodiment” does not necessarily refer to the same embodiment, althoughit may. Similarly, repeated use of the phrase “in some embodiments” doesnot necessarily refer to the same set or group of embodiments, althoughit may.

As used herein, and unless otherwise specified, the utilization ofordinal adjectives such as “first”, “second”, “third”, “fourth”, and soforth, to describe an item or an object, merely indicates that differentinstances of such like items or objects are being referred to; and doesnot intend to imply as if the items or objects so described must be in aparticular given sequence, either temporally, spatially, in ranking, orin any other ordering manner.

Some embodiments may be used in, or in conjunction with, various devicesand systems, for example, a Personal Computer (PC), a desktop computer,a mobile computer, a laptop computer, a notebook computer, a tabletcomputer, a server computer, a handheld computer, a handheld device, aPersonal Digital Assistant (PDA) device, a handheld PDA device, atablet, an on-board device, an off-board device, a hybrid device, avehicular device, a non-vehicular device, a mobile or portable device, aconsumer device, a non-mobile or non-portable device, an appliance, awireless communication station, a wireless communication device, awireless Access Point (AP), a wired or wireless router or gateway orswitch or hub, a wired or wireless modem, a video device, an audiodevice, an audio-video (A/V) device, a wired or wireless network, awireless area network, a Wireless Video Area Network (WVAN), a LocalArea Network (LAN), a Wireless LAN (WLAN), a Personal Area Network(PAN), a Wireless PAN (WPAN), or the like.

Some embodiments may be used in conjunction with one way and/or two-wayradio communication systems, cellular radio-telephone communicationsystems, a mobile phone, a cellular telephone, a wireless telephone, aPersonal Communication Systems (PCS) device, a PDA or handheld devicewhich incorporates wireless communication capabilities, a mobile orportable Global Positioning System (GPS) device, a device whichincorporates a GPS receiver or transceiver or chip, a device whichincorporates an RFID element or chip, a Multiple Input Multiple Output(MIMO) transceiver or device, a Single Input Multiple Output (SIMO)transceiver or device, a Multiple Input Single Output (MISO) transceiveror device, a device having one or more internal antennas and/or externalantennas, Digital Video Broadcast (DVB) devices or systems,multi-standard radio devices or systems, a wired or wireless handhelddevice, e.g., a Smartphone, a Wireless Application Protocol (WAP)device, or the like.

Some embodiments may comprise, or may be implemented by using, an “app”or application which may be downloaded or obtained from an “app store”or “applications store”, for free or for a fee, or which may bepre-installed on a computing device or electronic device, or which maybe otherwise transported to and/or installed on such computing device orelectronic device.

Functions, operations, components and/or features described herein withreference to one or more embodiments of the present invention, may becombined with, or may be utilized in combination with, one or more otherfunctions, operations, components and/or features described herein withreference to one or more other embodiments of the present invention. Thepresent invention may thus comprise any possible or suitablecombinations, re-arrangements, assembly, re-assembly, or otherutilization of some or all of the modules or functions or componentsthat are described herein, even if they are discussed in differentlocations or different chapters of the above discussion, or even if theyare shown across different drawings or multiple drawings.

While certain features of some demonstrative embodiments of the presentinvention have been illustrated and described herein, variousmodifications, substitutions, changes, and equivalents may occur tothose skilled in the art. Accordingly, the claims are intended to coverall such modifications, substitutions, changes, and equivalents.

1. A system comprising: a vehicular processor that is associated with avehicular Artificial Intelligence (AI) unit, that is configured: toreceive inputs from a plurality of vehicular sensors of a vehicle, tolocally process within said vehicle at least a first portion of saidinputs, to wirelessly transmit via a vehicular wireless transmitter atleast a second portion of said inputs to a remote tele-driving processorlocated externally to said vehicle, to wirelessly receive, via avehicular wireless receiver from said remote tele-driving processor, aremotely-computed processing result that is received from a remote node,and to implement a vehicular operating command based on saidremotely-computed processing result, via at least one of: (i) anautonomous driving unit of said vehicle, (ii) a tele-driving unit ofsaid vehicle, (iii) a vehicular driving processor, (iv) a vehicularcommands translator unit.
 2. The system of claim 1, wherein thevehicular processor that is associated with said vehicular AI unit is totransfer said vehicular operating command to an autonomous driving unitof said vehicle which then autonomously operates said vehicle based onsaid vehicular operating command.
 3. The system of claim 1, wherein thevehicular processor that is associated with said vehicular AI unit is totransfer said vehicular operating command to a tele-driving unit of saidvehicle which then autonomously operates said vehicle based on saidvehicular operating command.
 4. The system of claim 1, furthercomprising: a vehicular autonomous driving unit, configured toautonomously operate said vehicle based on both (I) inputs generatedlocally within said vehicle by said AI unit, and (II) said vehicularoperating command received from said remote tele-driving processor. 5.The system of claim 1, comprising: a dynamic encoder to dynamicallyencode said second portion of said inputs into a reduced-sizerepresentation prior to transmission to said remote tele-drivingprocessor.
 6. The system of claim 1, comprising: a data-sharingdetermination unit to dynamically determine which portions of saidinputs to transmit to said remote tele-driving processor and which otherportions of said inputs to process locally in said vehicular AI unit. 7.The system of claim 1, comprising: a data-sharing determination unit todynamically determine which portions of said inputs to transmit to saidremote tele-driving processor and which other portions of said inputs toprocess locally in said vehicular AI unit; wherein said data-sharingdetermination unit operates by taking into account at least a level ofcertainty of said vehicular AI unit in a locally-computed processingresult that was computed locally in said vehicle by said vehicular AIunit.
 8. The system of claim 1, comprising: a communication channelsallocator, to allocate a first set of packets having data from a firstvehicular sensor, for wireless transmission via a first set of one ormore vehicular wireless transmitters, and to allocate a second set ofpackets having data from a second vehicular sensor, for wirelesstransmission via a second set of one or more vehicular wirelesstransmitters.
 9. The system of claim 8, wherein the communicationchannels allocator is to dynamically determine which one or morevehicular transmitters are to be included in the first set oftransmitters, and which one or more vehicular transmitters are to beincluded in the second set of transmitters.
 10. The system of claim 8,wherein the communication channels allocator is to dynamicallydetermine, which one or more vehicular transmitters are to be includedin the first set of transmitters, and which one or more vehiculartransmitters are to be included in the second set of transmitters;wherein the first set of transmitters and the second set of transmittersinclude at least one particular transmitter that is common to both ofsaid sets.
 11. The system of claim 1, comprising: a communicationchannels allocator, to allocate a first wireless communication channelfor wireless transmission of data collected by a first vehicular sensorto a first remote processor for processing at said first remoteprocessor, and to allocate a second wireless communication channel forwireless transmission of data from a second vehicular sensor to a secondremote processor for processing at said second remote processor.
 12. Thesystem of claim 1, comprising: a vehicular multiple tele-operatorshandling unit, to wirelessly receive a first tele-driving command from afirst tele-driving processor, to wirelessly receive a secondtele-driving command from a second tele-driving processor, to detect aninconsistency between the first and second tele-driving commands,wherein said inconsistency comprises at least one of: contradiction,mismatch, duplication, adverse effects, contradictory results; and todetermine, based on pre-defined rules or by using the vehicular AI unit,which tele-operating command to discard and which tele-operating commandto execute in said vehicle.
 13. The system of claim 1, wherein saidremote tele-driving processor is a moving tele-driving processor that islocated in a secondary vehicle; wherein the vehicular AI unit is towirelessly transmit said second portion of inputs to the tele-drivingprocessor located in said secondary vehicle; wherein the vehicular AIunit is to wirelessly receive from said secondary vehicle theremotely-computed processing result; wherein the vehicular AI unit is togenerate said vehicular operating command based on saidremotely-computed processing result that was wirelessly received fromsaid secondary vehicle.
 14. The system of claim 1, wherein said remotetele-driving processor is a tele-driving processor that is external tosaid vehicle and is located in a non-mobile traffic infrastructureelement; wherein the vehicular AI unit is to wirelessly transmit saidsecond portion of inputs to the tele-driving processor located in saidnon-mobile traffic infrastructure element; wherein the vehicular AI unitis to wirelessly receive from said non-mobile traffic infrastructureelement the externally-computed processing result; wherein the vehicularAI unit is to generate said vehicular operating command based on saidremotely-computed processing result that was wirelessly received fromsaid non-mobile traffic infrastructure element.
 15. The system of claim1, further comprising: a communications-based map generator, to generatea communications-based map which indicates at least (i) a firstroad-segment having effective wireless communication throughput that isbelow a pre-defined threshold value, and (ii) a second road-segmenthaving effective wireless communication throughput that is above saidpre-defined threshold value.
 16. The system of claim 1, furthercomprising: a navigation route generator to generate or to modify anavigation route for said vehicle, by taking into account at least: anestimated level of wireless communication service availability atdifferent route segments.
 17. The system of claim 1, further comprising:a navigation route generator to generate or to modify a navigation routefor said vehicle, by taking into account at least: (i) an estimatedlevel of wireless communication availability at different routesegments, and also (ii) one or more constraints regarding safetyrequirements for passengers of said vehicle.
 18. The system of claim 1,further comprising: a navigation route generator, to increase safety oftravel of said vehicle which is a self-driving vehicle, by generating ormodifying a navigation route for said vehicle to include road-segmentshaving wireless communication availability that is above a pre-definedthreshold value.
 19. The system of claim 1, a navigation routegenerator, to increase safety of travel of said vehicle which supportsbeing tele-operated, by generating or modifying a navigation route forsaid vehicle to include road-segments in which tele-operation of saidvehicle is estimated to be successful.
 20. The system of claim 1,wherein a self-driving unit of said vehicle is to dynamically modify oneor more driving parameters of said vehicle, based on availability ofwireless communications at one or more particular route segments. 21.The system of claim 1, wherein a self-driving unit of said vehicle is todynamically modify one or more driving parameters of said vehicle, basedon estimated success level of remote tele-operation of said vehiclebased on tele-operating commands received from a remote machine-based orhuman tele-operator or from a remote AI processing unit.
 22. The systemof claim 1, wherein a self-driving unit of said vehicle is todynamically reduce a driving speed of said vehicle or to dynamicallyincrease a distance from a nearby vehicle, based on reduced availabilityof wireless communications at a particular route segment.
 23. The systemof claim 1, wherein a self-driving unit of said vehicle is todynamically reduce a driving speed of said vehicle or to dynamicallyincrease a distance from a nearby vehicle, based on a reduced estimatedlevel of success of remote tele-operation of said vehicle at aparticular route segment.
 24. The system of claim 1, wherein aself-driving unit of said vehicle is to dynamically reduce a drivingspeed of said vehicle or to dynamically increase a distance from anearby vehicle, at a particular route segment, based on reducedthroughput of wireless video upload from said vehicle to a remoterecipient that includes a remote tele-operation terminal or a remote AIprocessing unit.
 25. The system of claim 1, wherein an external AImodule, that is located externally to said vehicle, is to take over thedriving of said vehicle instead of said vehicular AI unit or instead ofan in-vehicle human driver.
 26. The system of claim 1, wherein anexternal AI module, that is located externally to said vehicle, is totake over the driving of said vehicle instead of said vehicular AI unitor instead of an in-vehicle human driver; wherein said external AImodule is to selectively refer the tele-operation of said vehicle to aremote human tele-operator.
 27. The system of claim 1, wherein anexternal AI module, that is located externally to said vehicle, is totake over the driving of said vehicle instead of said vehicular AI unitor instead of an in-vehicle human driver; wherein said external AImodule is to adjust a level of confidence in remote tele-operatingoperations, based on (i) quality of wireless communications receivedfrom said vehicle, and (ii) quality of sensed data that is sensed byvehicular sensors of said vehicle and is uploaded to said external AImodule.
 28. The system of claim 1, wherein data sensed by a singlevehicular sensor of said vehicle, is transmitted via a bondedcommunication uplink to an external AI unit that is located externallyto said vehicle, wherein packets that correspond to data sensed by saidsingle sensor of said vehicle, are uploaded from said vehicle by: (i)allocating a first set of packets to be uploaded via a first wirelesscommunication link by a first wireless transmitter associated with saidvehicle, and (ii) allocating a second set of packets to be uploaded viaa second wireless communication link by a second wireless transmitterassociated with said vehicle.
 29. The system of claim 1, wherein saidbonded communication uplink is utilized by said vehicle to increase aconfidence level of one or more remote AI modules that generatetele-operation commands for said vehicle based on data sensed by saidvehicle and uploaded to said one or more remote AI modules.
 30. Thesystem of claim 1, wherein said bonded communication uplink isdynamically constructed by said a link bonding unit associated with saidvehicle, by selecting two or more of any combination of: a cellularcommunication link, a Wi-Fi communication link, a V2X communicationlink, a satellite-based communication link, a Direct Short-RangeCommunication (DSRC) communication link.
 31. The system of claim 1,wherein the vehicular transmitter is to upload, to an external AI modulethat is external to said vehicle, data sensed by one or more vehicularsensors of said vehicle; wherein the vehicular AI unit is to receivefrom said external AI module, a batch of two or more conditionaltele-operating commands for said vehicle, and to perform a firsttele-operating command if a first condition holds true at a particularlevel of confidence, and to perform a second tele-operating command if asecond condition holds true at a particular level of confidence.
 32. Thesystem of claim 1, wherein a wireless transmitter associated with saidvehicle is to upload, to an external AI module that is external to saidvehicle, data sensed by one or more vehicular sensors of said vehicle;wherein the vehicular AI unit is to receive from said external AImodule, an authorization to perform in-vehicle processing of said senseddata and to avoid waiting for an incoming tele-operation command. 33.The system of claim 1, wherein a transmitter associated with saidvehicle is to upload, to an external AI module that is external to saidvehicle, data sensed by one or more vehicular sensors of said vehicle,wherein said data is at least partially processed within said vehicle toenable effective upload of said data to said external AI module based oneffective wireless communication resources that are currently availableto said vehicle using a multiplicity of wireless transceivers.
 34. Thesystem of claim 1, wherein a transmitter associated with said vehicle isto upload, to an external AI module that is external to said vehicle,data sensed by one or more vehicular sensors of said vehicle; wherein ifa quality indicator of effective wireless communication resources thatare currently available to said vehicle for said upload, is greater thana pre-defined threshold value, then the external AI module is todetermine a first level of certainty for a tele-operating commandgenerated by the external AI module; wherein if the quality indicator ofeffective wireless communication resources that are currently availableto said vehicle for said upload, is smaller than said pre-definedthreshold value, then the external AI module is to determine a second,reduced, level of certainty for said tele-operating command generated bythe external AI module.
 35. The system of claim 1, wherein a transmitterassociated with said vehicle is to upload, to an external AI module thatis external to said vehicle, data sensed by one or more vehicularsensors of said vehicle, wherein said transmitter comprises two or moretransmitting units, wherein said upload is performed over a bondedwireless communication link which comprises two or more wirelesscommunications links that are served concurrently by two or more,respective, wireless transmitting units that operate and uploaddifferent packets in parallel to each other.
 36. The system of claim 1,wherein two or more transmitters that are associated with said vehicleare to upload, to an external AI module that is external to saidvehicle, data sensed by one or more vehicular sensors of said vehicle;wherein a first transmitter is to wirelessly transmit, to said externalAI module, data sensed by a first vehicular sensor of said vehicle;wherein a second transmitter is to concurrently wirelessly transmit, tosaid external AI module, data sensed by a second vehicular sensor ofsaid vehicle.
 37. The system of claim 1, wherein two or more transmitterthat are associated with said vehicle are to upload, to an external AImodule that is external to said vehicle, data sensed by a singlevehicular sensor of said vehicle; wherein a first transmitter is towirelessly transmit, to said external AI module, a first batch ofpackets of data sensed by said single vehicular sensor of said vehicle;wherein a second transmitter is to concurrently wirelessly transmit, tosaid external AI module, a second batch of packets of data sensed bysaid single vehicular sensor of said vehicle.
 38. The system of claim 1,wherein a transmitter associated with said vehicle is to upload, to anexternal AI module that is external to said vehicle, data sensed by oneor more vehicular sensors of said vehicle, wherein said upload isperformed over a bonded wireless communication link which comprises anaggregation of two or more wireless communications links that are servedby two or more, respectively, wireless transmitters associated with saidvehicle.
 39. The system of claim 38, wherein said bonded wirelesscommunication uplink is dynamically constructed by a link bonding unitassociated with said vehicle, by selecting two or more communicationlinks in any combination out of: a cellular communication link, a Wi-Ficommunication link, a satellite-based communication link, a V2Xcommunication link, a Direct Short-Range Communication (DSRC)communication link; wherein the two or more communication links operateconcurrently and upload different packets in parallel to each other. 40.The system of claim 1, wherein the vehicular AI unit of said vehicle ispart of a multiple-peers network of AI units, which are distributedamong multiple vehicles and at least one AI unit that is external to allvehicles; wherein said multiple-peers network of AI units provides AItele-operating commands to at least one vehicle of said multiple-peersnetwork of AI units.
 41. The system of claim 1, wherein the vehicular AIunit of said vehicle operates (I) to perform AI processing of datasensed by sensors of another vehicle, and (II) to reach an AI-basedtele-operating command that is suitable for said other vehicle, (III) totransmit said tele-operating command to said other vehicle.
 42. Thesystem of claim 1, wherein commands that are sent from a remotetele-operation terminal to said vehicular processor of said vehicles,are transmitted over a multiplicity of wireless communicationtransceivers that are associated with said vehicular processor.
 43. Thesystem of claim 42, wherein said vehicular processor (i) operates toidentify two or more repeated tele-operating commands incoming via thesame transceivers or via different transceivers, and (ii) executes afirst command of said repeated tele-operating command, and (iii)discards one or more other commands of the repeated tele-operatingcommands.
 44. The system of claim 1, wherein two or more wirelesstransceivers, that are associated with said vehicle, and which havedifferent momentary performance characteristics, are concurrentlyutilized to upload data from said vehicle to a remote tele-operationterminal or to download data from said remote tele-operation terminal tosaid vehicle.
 45. The system of claim 1, wherein the two or morewireless transceivers, that are associated with said vehicle, havedifferent momentary performance characteristics of at least one of:throughput, good-put, effective upload bandwidth, effective downloadbandwidth, latency, delays, error rate, erroneous packet rate, missingpackets rate.
 46. The system of claim 1, wherein a link bonding unitthat is associated with said vehicle, operates to increase safety oraccuracy or certainty level of remotely-generated tele-operationcommands that are directed to said vehicle, by bonding together andallocating packets for upload across a multiplicity of wirelesscommunication transceivers that are available to said vehicle.
 47. Thesystem of claim 1, wherein at least a portion of packets, that representdata sensed by one or more vehicular sensors, is uploaded from saidvehicle to a remote tele-operation terminal or to a remote AI module,via a transceiver of a smartphone or a tablet of a non-driver passengerof said vehicle.
 48. The system of claim 1, wherein said systemcomprises said vehicle.
 49. An apparatus comprising: a cellularinfrastructure element comprising: (a) a cellular receiver to wirelesslyreceive from a vehicular transmitter of a vehicle packets correspondingto inputs of one or more vehicular sensors; (b) a data processorintegral to said cellular infrastructure element, to process saidpackets and to generate a vehicular operational command; (c) a wirelesstransmitter to wirelessly transmit to a wireless receiver of saidvehicle a signal indicating said vehicular operational command generatedby said cellular infrastructure element.
 50. The apparatus of claim 49,wherein the cellular infrastructure element comprises at least one of: acellular base station, a cellular transmission tower, an eNodeB element,a gNodeB element, a fixed non-moving cellular transceiver node, acellular edge infrastructure element, an edge computing element.
 51. Adevice comprising: a Wi-Fi access point comprising: (a) a Wi-Fi receiverto wirelessly receive from a vehicular transmitter of a vehicle packetscorresponding to inputs of one or more vehicular sensors; (b) a dataprocessor integral to said Wi-Fi access point, to process said packetsand to generate a vehicular operational command; (c) a wirelesstransmitter to wirelessly transmit to a wireless receiver of saidvehicle a signal indicating said a vehicular operational commandgenerated by said Wi-Fi access point.
 52. The device of claim 51,wherein said Wi-Fi access point is to locally process a first portion ofsaid packets, and to forward a second portion of said packets for remoteprocessing by a remote tele-driving processor.